Application recommendation method and related apparatus

Abstract

The application discloses an application recommendation method and related devices. In the method, an electronic device can determine recommendation probabilities of a plurality of applications, the recommendation probability of an application being a probability that a user uses the application. The electronic device can determine a plurality of recommended applications from the plurality of applications, the plurality of recommended applications including an application with the maximum recommendation probability in each of a plurality of application types. Then, the electronic device can display an application suggestion card, the application suggestion card displaying icons of the plurality of recommended applications. Implementing the method, the electronic device can expand the application type richness of the recommended applications as much as possible, improve the coverage of the recommended applications to the user demand in different scenarios, and increase the possibility that the recommended applications include a target application that the current user wants to use.

Description

Technical Field

[0001] This application relates to the field of terminal technology, and in particular to application recommendation methods and related devices. Background Technology

[0002] With the continuous development of terminal technology, electronic devices have gradually integrated into people's lives and become an indispensable part of them. Electronic devices typically display icons of all installed applications on their desktop. When a user intends to use a particular application, they generally need to find the icon of the target application among all the aforementioned application icons and perform an input operation (such as a touch operation) on the icon of the target application to trigger the electronic device to run the target application.

[0003] Currently, to meet diverse needs in various scenarios such as work, study, and social interaction, people install a large number of applications on their electronic devices. Due to the large number of applications on electronic devices, users may need to perform multiple swipes to find the icon of the target application among numerous pages, a time-consuming and laborious process. Therefore, how electronic devices can assist users in quickly finding the icon of the target application among a multitude of application icons is a worthy research direction in this field. Summary of the Invention

[0004] This application provides an application recommendation method and related apparatus. Implementing the method provided in this application can maximize the richness of recommended application types, improve the coverage of recommended applications to user needs in different scenarios, and increase the likelihood that recommended applications include target applications that the current user wishes to use.

[0005] In a first aspect, embodiments of this application provide an application recommendation method applied to an electronic device. The method includes: the electronic device determining the recommendation probability of multiple applications, whereby the recommendation probability indicates the probability that a user will use the application; the electronic device determining multiple recommended applications from the multiple applications, including the application with the highest recommendation probability in each of multiple application types; and the electronic device displaying an application suggestion card containing icons of the multiple recommended applications.

[0006] For example, application types may include one or more of the following: financial management, games, audio-visual entertainment, news reading, social communication, convenient living, shopping comparison, and sports and health.

[0007] Implementing the method provided in the first aspect, electronic devices, in determining recommended applications, consider not only the user's application usage habits but also the application type to which each application belongs. Since recommended applications include those with the highest recommendation probability across one or more application types, they are not only applications that the current user is highly likely to use but also applications of diverse types. This diversity in application types maximizes the richness of recommended applications, improves their coverage of user needs in different scenarios, and increases the likelihood that recommended applications include the target applications the current user desires.

[0008] In conjunction with the first aspect, in some implementations, multiple application types include: after determining the sum of the recommendation probabilities of applications contained in each application type in the electronic device, the multiple application types with the highest sum of recommendation probabilities.

[0009] Understandably, the application types with the highest sum of recommendation probabilities are those that users frequently use. Therefore, the recommended application types determined using the above method are likely to include the application types that the current user intends to use.

[0010] In conjunction with the first aspect, in some implementations, multiple application types include: after determining the number of applications contained in each application type in the electronic device, the multiple application types with the highest number of applications.

[0011] Understandably, the number of applications included in an application type can, to some extent, indicate a user's preference for that application type. Therefore, the recommended application types determined by the electronic device 100 using the above method are the application types that the user prefers to use. The recommended application types determined using the above method are highly likely to include the application types of the target application that the current user wishes to use.

[0012] In conjunction with the first aspect, in some implementations, the number of multiple application types is the minimum of the maximum number of application types used by the electronic device during a historical period and the maximum number of icons that the application recommendation card can accommodate.

[0013] In conjunction with the first aspect, in some implementations, multiple application types include: multiple application types whose sum of recommendation probabilities is greater than a threshold after determining the sum of recommendation probabilities of applications contained in each application type among the various application types contained in the electronic device.

[0014] In conjunction with the first aspect, in some implementations, if the number of applications with the highest recommendation probability under each of the multiple application types is less than the maximum number of icons that an application recommendation card can hold, then the multiple recommended applications also include one or more applications other than the applications with the highest recommendation probability under each of the multiple application types. The sum of the number of applications with the highest recommendation probability under each of the multiple application types and the number of the one or more applications equals the maximum number of icons that an application recommendation card can hold. Therefore, electronic devices can increase the number of recommended applications within the limitation of the maximum number of application icons that an application recommendation card can hold, thereby increasing the probability that a user will find the desired application among all recommended applications.

[0015] In conjunction with the first aspect, in some implementations, the aforementioned one or more applications refer to one or more applications with a high recommendation probability, excluding the applications with the highest recommendation probability under each of the aforementioned application types; or, the aforementioned one or more applications refer to the applications with the highest recommendation probability under each of some application types, excluding the applications with the highest recommendation probability under each of the aforementioned application types. The method for determining the aforementioned partial application types can refer to the aforementioned method for determining multiple application types, and will not be repeated here.

[0016] Understandably, by implementing the above method, electronic devices can increase the number of recommended applications as much as possible within the limit of the maximum number of application icons that the application recommendation card can hold, while ensuring that the recommended applications are those that users are most likely to use. This further increases the probability that users can find the application they need from all the recommended applications.

[0017] In conjunction with the first aspect, in some implementations, the application suggestion card includes multiple areas, with icons for recommended applications of different types displayed in different areas; or, the border colors of the icons for recommended applications of different types are different; or, the background patterns of the areas where the icons for recommended applications of different types are located are different. Therefore, when searching for the icon of a target application, users can quickly filter out icons of all applications of the same type as the target application in the application suggestion card based on these different display methods, and then search for the icon of the target application among these icons. Compared to searching for the icon of the target application among all the icons displayed in the application suggestion card, this narrows the search range and increases the speed at which users find the icon of the target application.

[0018] In conjunction with the first aspect, in some embodiments, the electronic device displays an application suggestion card, specifically including: the electronic device displays recommended applications in the application suggestion card. Upon receiving a first operation to expand the application suggestion card, the electronic device displays more recommended applications than previously listed. Therefore, when the user does not need to use the application suggestion card, it does not occupy too much space in the user interface. When the user needs to use the application suggestion card, it can expand it to display icons of more recommended applications, increasing the probability that the user can find the desired application from all the recommended applications.

[0019] In conjunction with the first aspect, in some embodiments, before the electronic device determines the recommendation probability of multiple applications, the method further includes: the electronic device acquiring usage data of the multiple applications, the usage data of which is used by the electronic device to determine the recommendation probability of the applications.

[0020] In conjunction with the first aspect, in some embodiments, before the electronic device determines the recommendation probabilities of multiple applications, the method further includes: the electronic device acquiring state data, the state data being used by the electronic device to determine the recommendation probabilities of the aforementioned applications, the recommendation probabilities of the applications indicating the probability that a user will use the application in the state indicated by the state data.

[0021] Status data may include one or more of the following: time data, network data, location data, weather data, context data, and device connection data.

[0022] In conjunction with the first aspect, in some implementations, the electronic device determines the recommendation probability of multiple applications, specifically including: the electronic device determines a first number of applications based on the usage data of the multiple applications, the first number of applications being the average number of applications activated by the electronic device per unit time.

[0023] If the number of the first applications is less than the first value, the electronic device can determine the recommendation probability of the multiple applications based on the activation probability of the multiple applications over a historical period obtained from the usage data of the above multiple applications.

[0024] If the number of the first applications is greater than or equal to a first value and less than a second value, the electronic device can determine the recommendation probability of the multiple applications by inputting the usage data of the aforementioned multiple applications into a first decision tree. The first decision tree is a decision tree trained by the electronic device using the usage data of the aforementioned multiple applications as input and the activation probability of the multiple applications over a historical period obtained based on the usage data of the aforementioned multiple applications as output.

[0025] If the first application is greater than or equal to the second value, the electronic device can process the usage data of the above multiple applications according to the multiple recall algorithm to determine the recommendation probability of the multiple applications. Attached Figure Description

[0026] Figure 1 A schematic diagram of the structure of the electronic device 100 provided in an embodiment of this application is shown;

[0027] Figure 2 This is a software structure block diagram of the electronic device 100 provided in the embodiments of this application;

[0028] Figure 3A and Figure 3B This is a set of user interface diagrams related to the display of application suggestion cards by the electronic device 100 provided in this application embodiment;

[0029] Figure 4 This is a flowchart of the application recommendation method provided in the embodiments of this application;

[0030] Figure 5A This is a schematic diagram illustrating how an electronic device 100 obtains the recommendation probability of an application based on application usage data, according to an embodiment of this application.

[0031] Figure 5B This is a schematic diagram of the first decision tree provided in an embodiment of this application;

[0032] Figure 5C This is a schematic diagram illustrating how another electronic device 100 provided in this application obtains recommendation probabilities for multiple applications based on application usage data;

[0033] Figure 6 This is a flowchart provided in this application embodiment for determining recommended applications based on the recommendation probabilities corresponding to the above-mentioned multiple applications and the application types of the multiple applications;

[0034] Figure 7 This is an internal interaction diagram of the electronic device 100 provided in the embodiments of this application. Detailed Implementation

[0035] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0036] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0037] The term "user interface (UI)" used in the following embodiments of this application refers to the medium interface through which an application or operating system interacts and exchanges information with the user. It realizes the conversion between the internal form of information and the form that the user can accept. The user interface is source code written in a specific computer language such as Java or Extensible Markup Language (XML). The interface source code is parsed and rendered on the electronic device, ultimately presenting content that the user can recognize. A common form of user interface is the graphical user interface (GUI), which refers to a user interface related to computer operation displayed graphically. It can be visible interface elements such as text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, and widgets displayed on the screen of an electronic device.

[0038] To help users quickly find the icon of a target application among numerous application icons and trigger the electronic device to run that application, the electronic device can determine the applications the user is likely to use currently based on the user's historical behavior. In this embodiment, the applications the user is likely to use currently can be referred to as recommended applications. The electronic device can then generate and display application suggestion cards, which may include the icons of the recommended applications.

[0039] Because electronic devices recommend apps based on user history, these are generally apps that users use frequently. For apps that are used less often but provide specific functions and are essential for users in certain scenarios, electronic devices typically won't recommend them using the above method. For example, users usually only need to use navigation apps when they are in an unfamiliar environment and need to determine their location and find their way to a destination. Therefore, navigation apps are not frequently used by users, and electronic devices usually won't recommend them based on user history. Consequently, when a user needs to use a navigation app, they won't be able to quickly find the app's icon through app suggestion cards. Instead, they will need to perform multiple swipes and carefully search through multiple pages to find the app's icon, and then input an action (such as a touch) to trigger the electronic device to run the target app. The whole process is time-consuming and laborious.

[0040] Therefore, this application provides application recommendation methods and related apparatus.

[0041] In this method, an electronic device can obtain the recommendation probability of multiple installed applications based on application usage data and current state data. Application usage data indicates the user's application usage habits, current state data indicates the current scenario of the electronic device, and recommendation probability indicates the likelihood of the user using the application. The electronic device can determine multiple recommended application types and identify the application with the highest recommendation probability within each type as the recommended application. Subsequently, the electronic device can generate and display application suggestion cards, which include icons of the recommended applications.

[0042] By implementing this method, electronic devices consider not only the user's application usage habits but also the application type of each application when determining recommended applications. Since the recommended applications include those with the highest recommendation probability across one or more application types, they are not only applications the current user is highly likely to use but also applications of diverse types. This diversity in application types maximizes the richness of the recommended applications, improves their coverage of user needs in different scenarios, and increases the likelihood that the recommended applications include the target applications the current user desires.

[0043] In addition, since the application suggestion cards displayed on electronic devices include the icons of the recommended applications mentioned above, users can quickly find the icon of the target application through the application suggestion cards, and then trigger the electronic device to run the target application by performing input operations (such as touch operations) on the icon of the target application. This shortens the time required for users to activate the target application and meets the user's current needs for the target application more quickly.

[0044] In some implementations, after identifying the most recommended apps across one or more app types, if the number of recommended apps is still less than the number of icons a suggestion card can hold, the electronic device can further identify the top one or more apps with the highest recommendation probability among those not yet identified as recommended apps before generating and displaying the suggestion card. This ensures that the number of recommended apps equals the number of icons a suggestion card can hold. Therefore, the electronic device can maximize the number of recommended apps within the maximum icon limit of the suggestion card, thereby increasing the probability that a user will find the desired app among all recommended apps.

[0045] In some implementations, icons for recommended apps of different app types can be displayed in different ways within the app suggestion card. These display methods can include the icon's position, border color, and background pattern of the area where the icon is located. Therefore, when searching for the icon of a target app, users can quickly filter the app suggestion card for icons of all apps of the same app type as the target app based on these different display methods, and then search for the target app's icon among those icons. Compared to searching for the target app's icon among all the icons displayed in the app suggestion card, this narrows the search scope and increases the speed at which users find the target app's icon.

[0046] The following describes the electronic devices involved in the embodiments of this application.

[0047] This application does not limit the specific type of electronic device. For example, the electronic device may include a mobile phone, as well as tablet computers, desktop computers, laptop computers, handheld computers, smart screens, wearable devices, augmented reality (AR) devices, virtual reality (VR) devices, artificial intelligence (AI) devices, in-vehicle systems, smart headphones, game consoles, and may also include Internet of Things (IoT) devices or smart home devices such as smart water heaters, smart lights, smart air conditioners, cameras, etc. It is not limited to these; the electronic device may also include non-portable terminal devices such as laptops and desktop computers with touch-sensitive surfaces or touch panels.

[0048] Figure 1 A schematic diagram of the structure of the electronic device 100 is shown.

[0049] Electronic device 100 may include processor 110, external memory interface 120, internal memory 121, universal serial bus (USB) interface 130, charging management module 140, power management module 141, battery 142, antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194, and subscriber identification module (SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0050] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0051] Processor 110 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0052] In this embodiment, the processor 110 can be used to obtain the recommendation probability of multiple applications installed on the electronic device 100 based on application usage data and current state data. Then, the processor 110 can determine the recommended application type and identify the application with the highest recommendation probability under that type as the recommended application. The specific manner in which the processor executes the above process can be found in the detailed description in the subsequent method embodiments, and will not be described here.

[0053] The controller can be the nerve center and command center of the electronic device 100. The controller can generate operation control signals according to the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0054] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0055] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0056] The wireless communication function of electronic device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

[0057] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0058] The mobile communication module 150 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 150 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processor 110 may be housed in the same device.

[0059] The modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs sound signals through an audio device (not limited to speaker 170A, receiver 170B, etc.) or displays images or videos through the display screen 194. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 110 and may be housed in the same device as the mobile communication module 150 or other functional modules.

[0060] The wireless communication module 160 can provide solutions for wireless communication applications on the electronic device 100, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0061] In this embodiment, the mobile communication module 150, wireless communication module 160, and wired communication module can be used to determine network data in the status data. The network data can be used to indicate the network connection status of the electronic device 100. The network data can be any of the following: not connected, connected to 4G, connected to 5G, or connected to WiFi. For example, when the mobile communication module 150 receives a 4G signal through antenna 1, the mobile communication module 150 can determine that the network data is "connected to 4G".

[0062] In addition, the aforementioned wireless communication module 160 and wired communication module can be used to determine device connection data within the status data. Device connection data can be used to indicate the device connection status of the electronic device 100. For example, the wireless communication module 160 and wired communication module can establish connections with other devices and receive device identifier and device type information sent by other devices. Based on the aforementioned device identifier and device type information, the wireless communication module 160 and wired communication module can generate and store device connection data.

[0063] In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, enabling electronic device 100 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the BeiDou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or satellite-based augmentation systems (SBAS). In this embodiment, antenna 1 couples to a mobile communication module 150, and antenna 2 couples to a wireless communication module 160, which can be used to support the electronic device 100 in communicating with the positioning device through the aforementioned wireless communication technologies, thereby determining the location data in the status data based on the communication.

[0064] Electronic device 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. Processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0065] Display screen 194 is used to display images, videos, etc. Display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD). The display panel can also be manufactured using organic light-emitting diodes (OLEDs), active-matrix organic light-emitting diodes (AMOLEDs), flexible light-emitting diodes (FLEDs), miniled, microled, micro-OLEDs, quantum dot light-emitting diodes (QLEDs), etc. In some embodiments, the electronic device may include one or N displays 194, where N is a positive integer greater than 1.

[0066] In this embodiment, the GPU can be used to generate application suggestion cards. The display screen 194 can be used to display the application suggestion cards, which include icons of recommended applications. In some implementations, during the generation of application suggestion cards, the GPU can display icons of recommended applications of different application types in different ways within the application suggestion cards. These display ways may include the icon's position, the icon's border color, and the background pattern of the area where the icon is located.

[0067] Electronic device 100 can perform shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0068] The external storage interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.

[0069] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of electronic device 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of electronic device 100 (such as audio data, phonebook, etc.). Furthermore, internal memory 121 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0070] In this embodiment, the internal memory 121 may store application usage data, which can indicate the user's application usage habits. A description of the application usage data can be found in the detailed descriptions in subsequent embodiments of this application, and will not be elaborated here.

[0071] Electronic device 100 can implement audio functions, such as music playback and recording, through audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, and application processor.

[0072] The software system of electronic device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. This application embodiment uses the layered architecture Android system as an example to exemplify the software structure of electronic device 100.

[0073] Figure 2 This is a software structure block diagram of the electronic device 100 according to an embodiment of this application.

[0074] A layered architecture divides software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom: the application layer, the application framework layer, the Android runtime and system libraries, and the kernel layer.

[0075] The application layer can include a series of application packages.

[0076] like Figure 2 As shown, the application package can include applications such as desktop management, perception, application recommendation, music, video, and SMS.

[0077] The following example, using application recommendation scenarios, illustrates the collaborative process of desktop management, perception, and application recommendation in this embodiment.

[0078] The sensor continuously monitors user actions. When a user triggers an application's execution by clicking on an area on the screen displaying an application icon, the sensor identifies the application and the application's start time, generating application usage data based on these two factors. The sensor can store this generated application usage data. In addition, the sensor can also determine state data. In some implementations, the sensor can retrieve the content of one or more data items in the state data by sending request messages to one or more modules in the application framework layer, and / or one or more applications in the application layer. For example, the sensor can send a request message to the location manager in the application framework layer to request the location data from the state data; it can also send a request message to a weather application to retrieve weather data from the state data.

[0079] The sensing system can send application usage data and status data to the application recommendation system. Upon receiving this data, the application recommendation system can determine the recommendation probability of multiple applications installed on the electronic device 100. Then, it can identify multiple application types and select the application with the highest recommendation probability within each type as the recommended application.

[0080] The application recommendation function can send its selected recommended applications to the desktop management system. The desktop management system can then generate application suggestion cards and send these cards to the display screen 194 to enable the display of the application suggestion cards. The aforementioned desktop management, perception, and application recommendation functions can include system applications or third-party applications.

[0081] In some implementations, the aforementioned desktop management, perception, and application recommendation can be applications that are upgraded based on existing applications.

[0082] It should be noted that the terms "desktop management," "perception," and "application recommendation" are merely names used in the embodiments of this application, and their meanings have been described in this embodiment. These names do not constitute any limitation on this embodiment. In some implementations, the functions provided by desktop management, perception, and application recommendation can be implemented by a single application, or distributed across multiple applications. This application embodiment does not limit this in any way.

[0083] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

[0084] like Figure 2 As shown, the application framework layer may include a location manager, network manager, window manager, content provider, view system, phone manager, resource manager, notification manager, etc. Among these,

[0085] The location manager is used to provide positioning and navigation functions for the electronic device 100. In this embodiment, the location manager can be used to provide location data within the status data. The location data may include GPS data, BDS data, Wi-Fi positioning data, etc.

[0086] The network manager is used to support network connectivity for the electronic device 100. In this embodiment, the network manager can generate network data in the status data based on the network connection status of the electronic device 100. For example, the network data can be any of the following: not connected, connected to 4G, connected to 5G, or connected to WiFi.

[0087] The window manager is used to manage windowed applications. It can retrieve screen size, determine the presence of a status bar, lock the screen, and capture screenshots, among other things.

[0088] Content providers are used to store and retrieve data, and make that data accessible to applications. This data may include videos, images, audio, phone calls made and received, browsing history and bookmarks, phone books, etc.

[0089] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0090] The phone manager is used to provide communication functions for electronic device 100. For example, it manages call status (including connection and disconnection).

[0091] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0092] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of completed downloads or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating electronic devices, and flashing indicator lights.

[0093] The Android Runtime consists of core libraries and a virtual machine. The Android runtime is responsible for scheduling and managing the Android system.

[0094] The core library consists of two parts: one part is the functionalities that need to be called by the Java language, and the other part is the Android core library.

[0095] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0096] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), etc.

[0097] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0098] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0099] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0100] A 2D graphics engine is a graphics engine for 2D drawing.

[0101] The kernel layer is the layer between hardware and software. The kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.

[0102] The following example illustrates the workflow of the software and hardware of electronic device 100.

[0103] When touch sensor 180K receives a touch operation, a corresponding hardware interrupt is sent to the kernel layer. The kernel layer processes the touch operation into a raw input event (including touch coordinates, timestamp of the touch operation, etc.). The raw input event is stored in the kernel layer. The application framework layer retrieves the raw input event from the kernel layer and identifies the control corresponding to the input event. Taking a touch click as an example, where the corresponding control is the camera application icon, the camera application calls the application framework layer's interface to launch the camera application, and then calls the kernel layer to launch the camera driver, capturing still images or videos through camera 193.

[0104] The user interface provided in the embodiments of this application is described below.

[0105] Figure 3A and Figure 3B An example is shown of a set of user interfaces involving an electronic device 100 displaying application suggestion cards.

[0106] Figure 3A The user interface 210 displayed on the electronic device 100 is shown.

[0107] The user interface 210 can be the desktop page of the electronic device 100. For example... Figure 3A As shown, the user interface 210 includes a status bar 211, a calendar and time indicator 212, a weather indicator 213, a page indicator 214, a tray 215 with icons of commonly used applications, and application suggestion cards 216A. Among them:

[0108] The status bar 211 may include: one or more signal strength indicators for mobile communication signals (also known as cellular signals), one or more signal strength indicators for Wi-Fi signals, battery status indicators, time indicators, etc.

[0109] The calendar and time indicator 212 are used to indicate the calendar and the current time.

[0110] Weather indicator 213 is used to indicate the weather.

[0111] Page indicator 214 can be used to indicate which page the user is currently viewing and the application icon is on. In this embodiment, application icons can be distributed across multiple pages, and users can swipe left and right to browse application icons on different pages.

[0112] Tray 215, featuring icons for commonly used applications, can be used to display: phone icon, message icon, contacts icon, camera icon, and more.

[0113] Application suggestion card 216A is one implementation of the application suggestion card, and can be used to display icons of recommended applications determined by electronic device 100. Figure 3A As we know, the size of the application suggestion card 216A is 2*2, meaning that the icons of recommended applications are displayed in a 2x2 arrangement. Therefore, the maximum number of recommended application icons that the application suggestion card 216A can hold is 2*2=4. The application suggestion card 216A can include icons for four applications: Application 1 ("Social Communication"), Application 2 ("Tools"), Application 3 ("Video / Audio"), and Application 4 ("Games").

[0114] App icons belonging to different app types can be displayed in different ways in the app suggestion card. For example, such as Figure 3A As shown in application suggestion card 216A, applications belonging to different application types have different border colors. Specifically, the border color of the application icon for the "Social Communication" application type is black, the border color of the application icon for the "Tools" application type is brown, the border color of the application icon for the "Multimedia" application type is blue, and the border color of the application icon for the "Games" application type is pink.

[0115] In addition, the application suggestion card 216A may also include an expansion control 217. The expansion control 217 can be used to trigger the electronic device 100 to expand the application suggestion card to a form that displays icons of more recommended applications. In some implementations, the expanded application suggestion card can be implemented as follows: Figure 3B The application suggestion card 216B in the user interface 210 shown.

[0116] It is understood that the application suggestion card 216A described above is merely one implementation example of the application suggestion card in this application. This application does not limit the specific shape, size, specifications, user interface location, or specific position of the application suggestion card within the user interface.

[0117] In some implementations, users can customize the user interface where the application suggestion card is located. For example, the user interface where the application suggestion card is located could be as follows: Figure 3A The user interface 210 shown can be the desktop page in the main interface, or it can be other pages in the main interface besides the desktop page, such as the negative one screen or the pull-down notification interface.

[0118] In some implementations, users can also set the specific position of the application suggestion card within its user interface. For example, in response to a long-press and drag operation on the application suggestion card, the electronic device 100 can move the application suggestion card to the endpoint of the long-press and drag operation within the user interface.

[0119] In some implementations, the electronic device 100 can delete or add application suggestion cards on the user interface in response to user operations. For example, in response to a long press operation on an application suggestion card, the electronic device 100 can display the application suggestion card in a floating position, and a delete control can be displayed in the upper right corner of the application suggestion card. In response to an input operation (such as a touch operation) on the delete control, the electronic device 100 can delete the application suggestion card on the user interface, and the application suggestion card will no longer be displayed on the user interface. Additionally, when the electronic device 100 is in a mode for editing the main interface content, in response to a long press and drag operation on an application suggestion card, the electronic device 100 can add the application suggestion card to the endpoint of the aforementioned long press and drag operation.

[0120] In some implementations, the electronic device 100 can provide a variety of application suggestion cards of different specifications for the user to choose from. For example, besides... Figure 3A The application suggestion card 216A shown is 2*2 in size. The electronic device 100 can also provide application suggestion cards with sizes of 2*4 and 2*5. For the description of the above two sizes of application suggestion cards, please refer to the relevant description of application suggestion card 216A in the foregoing embodiments. Except for the difference in icon arrangement and the maximum number of icons of recommended applications that can be accommodated, the other contents are the same, and will not be repeated here.

[0121] In some implementations, the electronic device 100 can continuously display application suggestion cards on the user interface for an extended period. In other implementations, the electronic device 100 will only display application suggestion cards on the user interface when it detects that the user has remained on the same main interface for more than a preset time. The preset time could be, for example, one minute.

[0122] In some implementations, the electronic device 100 may update the recommended apps at fixed time intervals and update the app suggestion card based on the updated recommended apps. For example, the fixed time interval could be 60 minutes.

[0123] Figure 3B An example is shown of the user interface involved in expanding application suggestion cards in electronic device 100.

[0124] The application suggestion card can be in a collapsed or expanded state. In some implementations, the application suggestion card is collapsed by default. The electronic device 100 can change the state of the application suggestion card in response to user actions. For example, the application suggestion card may include an expanded control. The electronic device 100 can change the state of the application suggestion card in response to input operations (such as touch operations) performed on the expanded control.

[0125] For example, the above Figure 3A The application suggestion card 216A in the user interface 210 can be an application suggestion card in a collapsed state. Figure 3B The application suggestion card 216B in the user interface 210 can be an expanded application suggestion card. The aforementioned expanded control can be implemented, for example, as follows: Figure 3A and Figure 3B The extended control 217 is among them.

[0126] In some implementations, the number of icons that an application suggestion card can hold is a fixed value, which can be determined by the manufacturer of the electronic device 100. For example, this fixed value could be 8. In other implementations, the number of icons that an application suggestion card can hold can be related to the state of the application suggestion card. Specifically, the number of icons that an application suggestion card can hold in a collapsed state can be indicated by the specifications of the application suggestion card. For example, such as... Figure 3A The 2x2 application suggestion card 216A shown in its collapsed state can hold 2x2 = 4 icons. The number of icons that the application suggestion card can hold in its expanded state can be a fixed value. This fixed value can be set by the manufacturer of the electronic device 100. For example, this fixed value can be 16.

[0127] like Figure 3B As shown, the expanded application suggestion card 216B may include icons for the following 16 applications: three applications of the "Social Communication" type: Application 1, Application 9, and Application 10; one application of the "Tools" type: Application 2; four applications of the "Audio-Visual Entertainment" type: Application 3, Application 11, Application 12, and Application 13; two applications of the "Shopping" type: Application 4 and Application 13; two applications of the "Finance and Management" type: Application 5 and Application 14; two applications of the "Sports and Health" type: Application 6 and Application 15; and two applications of the "News Reading" type: Application 7 and Application 16.

[0128] App icons belonging to different app types can be displayed in different ways in the app suggestion card. For example, such as Figure 3AAs shown in application suggestion card 216A, applications belonging to different application types have different border colors. Specifically, the border color of the application icon for the "Social Communication" application type can be black; the border color of the application icon for the "Tools" application type can be brown; the border color of the application icon for the "Audio / Video" application type can be blue; the border color of the application icon for the "Games" application type can be pink; the border color of the application icon for the "Shopping" application type can be green; the border color of the application icon for the "Finance and Management" application type can be red; the border color of the application icon can be yellow; and the border color of the application icon can be white.

[0129] for Figure 3B The descriptions of the other controls can be found in the foregoing embodiments. Figure 3A The descriptions of the same controls will not be repeated here.

[0130] The following describes the application recommendation method provided by the embodiments of this application.

[0131] like Figure 4 As shown, the application recommendation method provided in this application embodiment includes steps S101 to S105. Wherein,

[0132] S101, Electronic device 100 determines application usage data.

[0133] In response to a received user operation that triggers the electronic device 100 to run an application, the electronic device 100 can run the application and record and store application usage data. In this embodiment, the user operation that triggers the electronic device 100 to run an application can be referred to as an application running operation.

[0134] Application usage data may include the following data items: application identifier and time. The application identifier may be the identifier of the application launched by the electronic device 100 in response to the user's operation, and the time may be the time when the electronic device 100 starts running the application. For example, a piece of application usage data recorded and stored by the electronic device 100 may be implemented in the following form: "Camera, Thursday, July 28, 2022, 15:36".

[0135] Electronic device 100 can record and store one or more application usage data. The application usage data in electronic device 100 can indicate the user's application usage habits.

[0136] In some implementations, in response to a received application operation, the electronic device 100 can determine not only application usage data but also status data. In this embodiment, the status data determined by the electronic device 100 in response to the received application operation can be referred to as first status data. Application usage data may include the aforementioned first status data. For a detailed description of the content of the first status data, please refer to the relevant description of the content of the status data in the subsequent step S102; it will not be described in detail here.

[0137] (Optional) S102, Electronic device 100 determines status data.

[0138] In some implementations, electronic device 100 can determine status data in response to an operation that triggers the display of a user interface including application suggestion cards. In some implementations, electronic device 100 can determine status data when it detects that a user has remained on the same main interface for more than a preset time. In other implementations, electronic device 100 can continuously perform this operation or determine status data at fixed intervals.

[0139] Status data can indicate the scenario in which the electronic device 100 is located. In this embodiment, status data may include one or more of the following data items: time data, network data, location data, weather data, context data, and device connection data.

[0140] Time data can indicate time.

[0141] Network data can indicate the network connectivity status of electronic device 100. For example, network data can be any of the following: not connected, connected to 4G, connected to 5G, or connected to WiFi.

[0142] Location data can be used to indicate the geographical location of electronic device 100. Location data can be GPS data, BDS data, Wi-Fi positioning data, etc. Location data can be expressed as latitude and longitude values.

[0143] Weather data can be used to indicate the weather conditions at the geographical location of electronic device 100. For example, the weather data can be any of the following: sunny, cloudy, overcast, rainy, or snowy.

[0144] Context data can be used to indicate applications enabled by electronic device 100 before and / or after receiving the application run operation. For example, electronic device 100 may determine the identifiers of five applications enabled before receiving the application run operation as context data.

[0145] Device connection data can be used to indicate the device connection status of electronic device 100. For example, device connection data may include the identifiers and device types of all devices connected to electronic device 100.

[0146] It is understood that the above-mentioned application use data and status data are merely terms used in the embodiments of this application, and their meanings and uses have been recorded in the embodiments. Their names do not constitute any limitation on the embodiments.

[0147] In this embodiment of the application, step S102 is an optional step.

[0148] S103. Electronic device 100 obtains the recommendation probability of the application based on application usage data, or application usage data and status data.

[0149] This application provides four implementation methods for the process by which an electronic device 100 obtains the recommendation probability of an application based on application usage data, or application usage data and status data. The four implementation methods will be described in detail below.

[0150] In implementation method 1, the electronic device 100 can statistically analyze application usage data within a first time period to obtain the activation probability of each application within that first time period. The electronic device 100 can then determine the activation probability of the applications within that first time period as the recommendation probability of the applications.

[0151] In some implementations, the electronic device 100 can determine the application recommendation probability solely based on application usage data. Specifically, the aforementioned "first time" can be a preset time period, such as the last 30 days, the last 7 days, or the last 24 hours.

[0152] In other implementations, the electronic device 100 can obtain the recommendation probability of an application based on application usage data and status data. Specifically, the first time can be a time that corresponds to the time indicated by the time data in the status data. Such a correspondence can be, for example, the same weekday (e.g., Monday, Tuesday, ..., Sunday), the same hourly time (e.g., 7:00-8:00, 8:00-9:00, ..., 23:00-24:00), etc.

[0153] Electronic device 100 can first statistically analyze application usage data within a first time period to obtain the activation frequency of each application within that time period. Then, the ratio of the activation frequency of each application within the first time period to the sum of the activation frequencies of all applications can be used to determine the recommendation probability of that application.

[0154] Specifically, taking the process of determining the recommendation probability of application 1 by electronic device 100 as an example: Figure 5AAs shown, if the time data in the status data indicates an hourly time period of 7:00-8:00, then the electronic device 100 can filter all application usage data stored therein, identifying the application as application 1 and specifying that the time falls within the first time period. If the electronic device 100 filters out 2 pieces of application usage data, then the electronic device 100 can determine that application 1 is activated 2 times within the first time period. Then, the electronic device 100 can calculate the activation probability of application 1 within the first time period, i.e., the ratio of the activation frequency of application 1 within the first time period to the sum of the activation frequencies of all applications: 2 / (2+6+3+2+7+2+1+1+1+0) = 0.08, and determine the calculated result 0.08 as the recommended probability of application 1.

[0155] In implementation method 2, electronic device 100 uses a decision tree algorithm to train a first decision tree based on application usage data. Electronic device 100 then inputs the state data into the first decision tree to obtain the recommendation probability for each application.

[0156] Electronic device 100 can obtain a first decision tree through training using a decision tree algorithm. During the splitting process of the decision tree node, electronic device 100 can determine the best feature for the current split and its corresponding feature value through the decision tree algorithm, and split the application data according to the best feature and its corresponding feature value.

[0157] The electronic device 100 determines the optimal feature and its corresponding feature value, which is the process by which the electronic device 100 selects a feature and its corresponding feature value from the application usage data as the splitting criterion for the current node. Feature selection during the decision tree node splitting process can use metrics such as information gain, information gain ratio, and Gini index as evaluation criteria. Different decision tree algorithms correspond to different evaluation criteria. For example, the third-generation iterative dichotomiser 3 (ID3) algorithm uses information gain as the evaluation criterion for feature selection, the C4.5 algorithm uses information gain ratio, and the classification and regression tree (CART) algorithm uses the Gini index. In some implementations, the decision tree algorithm used by the electronic device 100 can be determined by the manufacturer of the electronic device 100 and stored within the electronic device 100.

[0158] After obtaining the first decision tree, the electronic device 100 can input the state data into the first decision tree to obtain the recommendation probability of each application.

[0159] For example, Figure 5B This is a schematic diagram of a first decision tree structure provided in an embodiment of this application. For example... Figure 5BAs shown, the first decision tree extends downwards from the root node 0, forming a four-layer tree structure. The first layer includes node 0; the second layer includes nodes 1 and 2; the third layer includes nodes 3, 4, 9, and 10; and the fourth layer includes nodes 7, 8, 5, 6, 11, and 12. After training the first decision tree based on application usage data, the electronic device 100 can input state data into the first decision tree to obtain the recommendation probability for each application.

[0160] In implementation method 3, electronic device 100 uses a multi-path recall algorithm to obtain the application recommendation probability based on application usage data and status data.

[0161] like Figure 5C As shown, the process by which electronic device 100 obtains the recommendation probabilities of multiple applications based on application usage data through a multi-path recall algorithm can include two steps, wherein:

[0162] Step 1: Electronic device 100 categorizes application usage data into multiple categories based on time. Electronic device 100 can process each category using different paths to obtain multiple initial recommendation probabilities for each application.

[0163] For example, electronic device 100 can categorize its stored application usage data into three types based on time: long-term application usage data, recent application usage data, and real-time application usage data.

[0164] The time data items in long-term application usage data indicate times within a long-term timeframe. The time data items in recent application usage data indicate times within a recent timeframe. The aforementioned long-term and recent times can be preset by the manufacturer of the electronic device 100, and this embodiment does not specifically limit them. For example, the aforementioned long-term timeframe can be the past 3 months. The aforementioned recent timeframe can be the past week.

[0165] The real-time application usage data includes application identifiers, which vary from data item to data item. Furthermore, the time data item indicates the time of one or more application usage data entries within the real-time timeframe. The aforementioned real-time time can also be a preset time by the manufacturer of the electronic device 100; this embodiment does not specifically limit it. For example, the aforementioned real-time time can be approximately 24 hours.

[0166] Electronic device 100 can process long-term application usage data using a first path to obtain a first initial recommendation probability for each application; process recent application usage data using a second path to obtain a second initial recommendation probability for each application; and process real-time application usage data using a third path to obtain a third initial recommendation probability for each application. Among these,

[0167] The aforementioned first path instructs the electronic device 100 to input long-term application usage data into a decision tree to obtain a first initial recommendation probability for each application. In some implementations, the aforementioned decision tree may be the first decision tree mentioned in the foregoing embodiments. For a description of the decision tree, please refer to the relevant descriptions in the foregoing embodiments; further details will not be repeated here.

[0168] The second path instructs the electronic device 100 to input recent application usage data into a popular application recall algorithm to obtain a second initial recommendation probability for each application. The aforementioned popular application recall algorithm can be an algorithm obtained by the electronic device 100 from a server. In one possible implementation, the recent popular application recall algorithm can be the method described in implementation 1 of the foregoing embodiments.

[0169] The third path instructs electronic device 100 to process real-time application usage data using a time decay algorithm to obtain a third initial recommendation probability for each application. Specifically, for any piece of real-time application usage data, electronic device 100 can calculate the time difference between the time indicated by the time data item in that piece of application usage data and the time indicated by the time data in the status data. Electronic device 100 can determine the third initial recommendation probability for each application according to the following formula: Where N(t) i Let N0 be the third initial recommendation probability of the application; α be the initial decay value; l be the exponential decay constant; t be the time difference mentioned above. Here, N0, α, and l are all constant values.

[0170] Step 2: Electronic device 100 obtains the recommendation probability of the application based on the recall coefficient of each path and the multiple initial recommendation probabilities of each application mentioned above.

[0171] For any given application, the electronic device 100 can determine the application's recommendation probability by summing the products of the recall coefficients for each path and the initial recommendation probability of the application obtained based on that path. In some implementations, the recall coefficients for each path can be preset by the manufacturer of the electronic device 100 and stored in the electronic device 100.

[0172] In the case where the electronic device 100 divides application usage data into three categories according to time, and processes the above three categories using a first path, a second path, and a third path respectively, the recall coefficient of the first path can be, for example, 0.2, the recall coefficient of the second path can be, for example, 0.3, and the recall coefficient of the third path can be, for example, 0.5.

[0173] Taking the process of determining the recommendation probability of a camera application by electronic device 100 as an example: if the first initial recommendation probability of the camera application obtained by electronic device 100 according to the first path is 0.06, the second initial recommendation probability of the camera application obtained according to the second path is 0.084, and the third initial recommendation probability of the camera application obtained according to the third path is 0.15, then the recommendation probability of the camera application is 0.06*0.2+0.084*0.3+0.15*0.5=0.112.

[0174] Implementation method 4: Electronic device 100 obtains the first number of applications based on application usage data, and uses the strategy corresponding to the first number of applications to obtain the recommendation probability of the applications based on application usage data and status data.

[0175] Because different users may have different habits and ways of using applications, the frequency of application use may also vary. For example, some users use a relatively limited range of applications, typically using only 0-10 applications per day on average; this group accounts for about 50% of all users. Another group uses 11-15 applications per day on average; this group accounts for about 36% of all users. Still others have a wide range of interests and use 16 or more applications per day on average; this group accounts for about 14% of all users.

[0176] For users with varying frequencies of app usage, electronic device 100 can employ different strategies to determine the app recommendation probability.

[0177] In some implementations, the electronic device 100 can use the number of applications used as a user segmentation indicator to formulate different recommendation strategies for different user groups. This allows users who use fewer applications to use a more efficient recommendation strategy, while users who use more applications can use a more accurate recommendation strategy. In this way, the accuracy of application recommendations can be balanced with the efficiency of recommendations. Specifically:

[0178] First, the electronic device 100 can obtain the first number of applications based on application usage data.

[0179] The first application count is the average number of applications activated by the electronic device 100 per unit time within a second time period. The second time period is greater than or equal to this unit time, and both the second time period and the unit time period can be preset by the manufacturer of the electronic device 100. For example, the second time period could be the past week, the past month, the past two months, the past three months, etc. The unit time period could be the past day, the past 12 hours, etc. Understandably, the first application count can indicate the frequency with which the electronic device 100 runs applications.

[0180] Specifically, the process by which electronic device 100 obtains the first number of applications based on application usage data is as follows: Electronic device 100 can statistically analyze the application usage data where the time indicated by the time data item is located in the second time period to obtain the number of applications activated by electronic device 100 in each unit time period within the second time period. Then, electronic device 100 can determine the average number of applications activated by electronic device 100 in each unit time period as the first number of applications.

[0181] For example, the second time period can be the past two months, and the unit time period can be the past day. Electronic device 100 can statistically determine the following: In the past two months, electronic device 100 used 12 applications on 15 days; 14 applications on 15 days; 10 applications on 15 days; and 8 applications on 15 days. Therefore, the average number of applications used daily over the past two months, which is 11, can be determined as the first number of applications.

[0182] Different numbers of first applications can correspond to different strategies. For example, a first application number ≤ a first value can correspond to a first strategy; a first value < first application number ≤ a second value can correspond to a second strategy; and a first application number > a second value can correspond to a third strategy. The first and second values ​​are preset values, and the first value is less than or equal to the second value. In some implementations, the first and second values ​​can be determined by the manufacturer of the electronic device 100 and stored in the electronic device 100. For example, the first value can be 10, and the second value can be 15.

[0183] The first strategy described above can be, for example, the implementation method 1 described in the foregoing embodiments; the second strategy can be, for example, the implementation method 2 described in the foregoing embodiments; and the third strategy can be, for example, the implementation method 3 described in the foregoing embodiments.

[0184] After obtaining the first number of applications based on application usage data, the electronic device 100 can use the strategy corresponding to the first number of applications to obtain the recommendation probability of the applications based on the application usage data. For example, if the first number of applications is 12, then the electronic device 100 can use the second strategy corresponding to the first number of applications, i.e., implementation method 2 described in the foregoing embodiments, to obtain the recommendation probability of the applications based on the application usage data.

[0185] It is understood that the four implementation methods described above are merely examples provided in the embodiments of this application. The embodiments of this application do not limit the specific method by which the electronic device 100 obtains the recommendation probability of an application based on application usage data, or application usage data and status data.

[0186] S104. Electronic device 100 determines the recommended application based on the recommendation probability and application type of the above-mentioned application.

[0187] like Figure 6 As shown, the process of electronic device 100 executing step S104 may include the following steps:

[0188] Step 1: Electronic device 100 determines the application type of the application.

[0189] In some implementations, the application type can be determined by the application developer or the platform providing the application download service. In this case, the electronic device 100 can receive information indicating the application type at the same time as receiving the application installation package sent by the application developer or the platform's server, and determine the application type accordingly. In other implementations, the application type can be determined by the user. The electronic device 100 can provide a page for the user to select the application type. In response to received user actions to select an application and select an application type, the electronic device 100 can determine the application type based on these user actions.

[0190] An application's application type can be used to indicate the application's functionality. In some implementations, an application may provide one function, or multiple functions simultaneously. Therefore, an application may belong to one, or multiple application types.

[0191] Application types can include: financial management, games, audio-visual entertainment, news reading, social communication, convenient living, shopping comparison, sports and health, etc.

[0192] Step 2: Electronic device 100 determines the recommended application type.

[0193] First, the electronic device 100 can determine the number of recommended application types. The number of recommended application types can be the minimum of the number of application types used by the electronic device 100 during a historical period and the number of icons that can be accommodated in the application recommendation card. For a description of the number of icons that can be accommodated in the application recommendation card, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0194] For example, if the number of application types used in the electronic device 100 during a historical period is 5, and the number of charts that the application recommendation card can hold is 4, then the electronic device 100 can determine that the number of recommended application types is 4.

[0195] After determining the number of recommended application types, the electronic device 100 can specifically determine the recommended application types based on the number of recommended application types mentioned above.

[0196] In some implementations, the electronic device 100 can determine the recommended application type based on the sum of the recommendation probabilities of all applications in each application type. Specifically, the electronic device 100 can determine the top one or more application types with the highest sum of recommendation probabilities of all applications as the recommended application type.

[0197] Understandably, the recommended application types determined by electronic device 100 are application types that users frequently use. The recommended application types determined using the above method are highly likely to include the application type of the target application that the current user intends to use.

[0198] In other implementations, the electronic device 100 can determine the recommended application types based on the number of applications included in each application type. Specifically, the electronic device 100 can determine the top one or more application types with the largest number of included applications as the recommended application types.

[0199] Since the number of applications included in an application type can, to some extent, indicate a user's preference for that application type, the recommended application types determined by the electronic device 100 using the above method are likely to be the application types the user prefers to use. The recommended application types determined using the above method are highly likely to include the application types of the target application that the user currently wishes to use.

[0200] In some implementations, electronic device 100 can determine the sum of the recommendation probabilities of applications within each of all application types it contains. Then, electronic device 100 can determine application types whose sum of recommendation probabilities exceeds a certain threshold as recommended application types. This threshold can be determined by the manufacturer of electronic device 100.

[0201] In this scenario, if the number of recommended application types determined by the electronic device 100 using the above method exceeds the number of icons that the application suggestion card can hold, then the electronic device 100 can filter the determined recommended application types so that the number of filtered recommended application types equals the number of icons that the application suggestion card can hold. For example, the electronic device 100 can filter based on the sum of the recommendation probabilities of all applications within each recommended application type, retaining the top few recommended application types with the highest sum of recommendation probabilities and deleting one or more recommended application types with the lowest sum of recommendation probabilities, thus ensuring that the number of filtered recommended application types equals the number of icons that the application suggestion card can hold.

[0202] Step 3: Electronic device 100 determines the application with the highest recommendation probability among all recommended application types as the recommended application.

[0203] After determining the recommended application type, the electronic device 100 can identify the application with the highest recommendation probability among each recommended application type as the recommended application.

[0204] For example, when electronic device 100 performs step 2, it can determine the application type "Tools" as the recommended application type. Since the recommended application type "Tools" includes two applications, application 2 and application 10, and the recommendation probability of application 2 is greater than that of application 10, electronic device 100 can determine application 2 in the recommended application type "Tools" as the recommended application.

[0205] Since the recommended applications determined by the electronic device 100 include the applications with the highest recommendation probability under each application type, the recommended applications are not only those that the current user is highly likely to use, but also applications of various types. The diverse application types of the recommended applications maximize the richness of the recommended applications, improve the coverage of user needs in different scenarios, and increase the likelihood that the recommended applications include the target applications that the current user intends to use.

[0206] (Optional) Step 4: Electronic device 100 determines whether the number of recommended applications is less than the number of icons that the application suggestion card can hold.

[0207] In this embodiment, step 4 is an optional step. If the electronic device 100 determines that the number of recommended applications is less than the number of icons that the application suggestion card can hold, then the electronic device 100 can execute step 5.

[0208] (Optional) Step 5: If the number of recommended applications is less than the number of icons that the application suggestion card can hold, the electronic device 100 will identify one or more applications that have not yet been identified as recommended applications as recommended applications, so that the number of recommended applications is equal to the number of icons that the application suggestion card can hold.

[0209] If the number of recommended apps is still less than the number of icons that the app suggestion card can hold, the electronic device 100 can further identify one or more apps that have not yet been identified as recommended apps, so that the number of recommended apps equals the number of icons that the app suggestion card can hold. Thus, the electronic device 100 can increase the number of recommended apps as much as possible within the maximum number of app icons that the app suggestion card can hold, thereby increasing the probability that the user will find the app they need from all the recommended apps.

[0210] Specifically, in some implementations, after performing step 3 above, if the number of recommended applications is still less than the number of icons that the application suggestion card can hold, the electronic device 100 can randomly identify one or more applications as recommended applications from among those that have not yet been identified as recommended applications, so that the number of recommended applications is equal to the number of icons that the application suggestion card can hold.

[0211] In some implementations, after performing step 3 above, if the number of recommended applications is still less than the number of icons that the application suggestion card can hold, the electronic device 100 can identify one or more of the applications with the highest recommendation probability among those that have not yet been identified as recommended applications as recommended applications, so that the number of recommended applications is equal to the number of icons that the application suggestion card can hold.

[0212] In some implementations, after performing step 3, the electronic device 100 can remove the recommended applications determined by performing step 3 from its application type. Then, the electronic device 100 can perform steps 2 and 3 again. Specifically, when performing step 2, the electronic device 100 can determine the number of recommended application types by the difference between the number of icons that the application suggestion card can hold and the number of already determined recommended applications. Otherwise, the process of the electronic device 100 performing steps 2 and 3 again can be referred to the description in the aforementioned embodiments, and will not be repeated here.

[0213] Therefore, the electronic device 100 can increase the number of recommended applications as much as possible within the limit of the maximum number of application icons that the application recommendation card can hold, thereby increasing the probability that the user can find the application they need from all the recommended applications.

[0214] Since an application can belong to one or more application types, the recommended applications determined by electronic device 100 may include multiple duplicate applications. Therefore, in some implementations, after performing the aforementioned steps 3 and / or 5, electronic device 100 can deduplicate the recommended applications, removing duplicate applications to obtain the final recommended applications.

[0215] In some implementations, after executing step S104, the electronic device 100 may send a request to the server, requesting the server to send it a server-recommended application. The server may be a server provided by the manufacturer of the electronic device 100, a server provided by a platform offering application download services, etc. The server-recommended application includes one or more applications determined by the server. After receiving the server-recommended application sent by the server, the electronic device 100 may determine the server-recommended application as a recommended application, or modify a previously determined recommended application based on the server-recommended application.

[0216] Therefore, when the electronic device 100 is used for a short period of time, resulting in little or no application usage data stored on it, the electronic device 100 can use the widely applicable recommended applications provided by the server as a reference to determine the recommended applications. This can improve the accuracy of the electronic device 100 in determining the recommended applications.

[0217] S105, Electronic device 100 generates and displays an application suggestion card.

[0218] An application suggestion card is a control used to display icons of recommended applications. After determining the recommended applications, the electronic device 100 can generate and display an application suggestion card based on these applications. The application suggestion card may include icons of one or more of the recommended applications. The electronic device 100 can then display the generated application suggestion card.

[0219] The form of an application suggestion card may be related to its specifications and status. For a description of the relationship between the form, specifications, and status of the application suggestion card, please refer to the relevant descriptions in the foregoing embodiments; they will not be repeated here.

[0220] Since the recommended applications are determined by the electronic device 100 based on user behavior and application type, they represent applications the user is likely to use currently. Therefore, the probability that the user's current desired application is one of the recommended applications is relatively high. The user can find the icon of the target application among one or more icons of recommended applications displayed on the application suggestion card, and then trigger the electronic device to run the target application by performing an input operation (such as a touch operation) on the icon. Compared to searching for the icon of the target application among the vast number of applications installed on the electronic device 100, this significantly reduces the user's search time and more quickly meets the user's current need for the target application.

[0221] In some implementations, icons for recommended apps of different app types can be displayed in different ways within the app suggestion card. These display methods can include the icon's position, the icon's border color, and the background pattern of the area where the icon is located.

[0222] Specifically, in the case where the display method is an icon, the application suggestion card generated by the electronic device 100 may include multiple areas, and any one of these areas corresponds to an application type. The electronic device 100 may display the recommended application in the area corresponding to that application type.

[0223] In some examples, the area within the application suggestion card changes flexibly based on the number of recommended applications within that area's application type. In other examples, the area within the application suggestion card remains fixed. The area may not be completely filled with application icons. When the number of recommended applications within the application type of an area exceeds the maximum number of application icons that the area can hold, the electronic device 100 can select a subset of applications from the recommended applications within that area's application type and display the icons of those selected applications within that area.

[0224] Therefore, when searching for the icon of a target application, users can quickly filter the application suggestion card to find icons of all applications of the same type as the target application based on the different display methods mentioned above, and then search for the icon of the target application among those icons. Compared to searching for the icon of the target application among all the icons displayed in the application suggestion card, this narrows the search range and increases the speed at which users find the icon of the target application.

[0225] Figure 7 An internal interaction diagram of an electronic device 100 provided in an embodiment of this application is shown.

[0226] like Figure 7 As shown, the electronic device 100 may include a sensing module 1001, an application recommendation module 1002, and a display module 1003.

[0227] The following will combine Figure 7 This section describes in detail the collaboration process between the various modules of the electronic device 100 during the execution of the application recommendation method provided in this embodiment. Step 1 can correspond to the aforementioned... Figure 4 Step S101 in the above. Step 2 can correspond to the aforementioned Figure 4 Step S102 in the above. Step 4 can correspond to the aforementioned Figure 4 Step S103 in the above. Step 5 can correspond to the aforementioned Figure 4 Step S104 in the above. Step 7 can correspond to the aforementioned Figure 4 Step S105 in the process.

[0228] Step 1: In response to the received application operation, the sensing module 1001 determines the application usage data.

[0229] The sensing module 1001 can continuously monitor user operations, which may include running applications. For a description of running applications, please refer to the relevant descriptions in the foregoing embodiments; they will not be repeated here.

[0230] In response to a received application operation, the sensing module 1001 can record and store a piece of application usage data. For a description of the application usage data, please refer to the relevant descriptions in the foregoing embodiments; they will not be repeated here. The sensing module 1001 can record and store one or more pieces of application usage data. Application usage data can indicate a user's application usage habits.

[0231] In some implementations, in response to a received application operation, the sensing module 1001 can determine not only the application usage data but also the status data. In this embodiment, the status data determined by the sensing module 1001 in response to the received application operation can be referred to as first status data. A description of the first status data can be found in the descriptions of status data in the foregoing embodiments, and will not be repeated here.

[0232] (Optional) Step 2: Sensing module 1001 determines the status data.

[0233] In some implementations, the sensing module 1001 can determine status data in response to an operation that triggers the electronic device 100 to display a user interface including application suggestion cards. In some implementations, the sensing module 1001 can determine status data when it detects that a user has remained on the same main interface for more than a preset time. In other implementations, the sensing module 1001 can continuously perform operations during the operation of the electronic device 100, or determine status data at fixed intervals.

[0234] For a description of the status data, please refer to the description of the status data in the foregoing embodiments, which will not be repeated here.

[0235] In this embodiment of the application, step 2 is optional.

[0236] Step 3: The perception module 1001 sends the above status data to the application recommendation module 1002.

[0237] Step 4: The application recommendation module 1002 obtains the application recommendation probability based on application usage data, or application usage data and status data.

[0238] The process by which the application recommendation module 1002 obtains the application recommendation probability based on application usage data, or application usage data and status data, can be referred to the relevant description in the foregoing embodiments, and will not be repeated here.

[0239] Step 5: The application recommendation module 1002 determines the recommended application based on the above recommendation probability and application type.

[0240] Step 5 may include five sub-steps: Step 5(a) to Step 5(e). Wherein:

[0241] Step 5(a): The application recommendation module 1002 determines the application type of the application.

[0242] The process by which the application recommendation module 1002 determines the application type of an application can be referred to the relevant description in the foregoing embodiments, and will not be repeated here.

[0243] Step 5(b): The application recommendation module 1002 determines the recommended application type.

[0244] First, the application recommendation module 1002 can determine the number of recommended application types. The number of recommended application types can be the minimum of the number of application types used by the electronic device 100 during a historical period and the number of icons that the application recommendation card can hold. For a description of the number of icons that the application recommendation card can hold, please refer to the relevant descriptions in the foregoing embodiments; they will not be repeated here.

[0245] After determining the number of recommended application types, the electronic device 100 can specifically determine the recommended application types based on the aforementioned number. In some implementations, the electronic device 100 can determine the recommended application types based on the sum of the recommendation probabilities of all applications within each application type. In other implementations, the electronic device 100 can specifically determine the recommended application types based on the number of applications included in each application type. For a description of the process by which the electronic device specifically determines the recommended application types based on the aforementioned number of recommended application types, please refer to the relevant descriptions in the foregoing embodiments; they will not be repeated here.

[0246] Step 5(c): The application recommendation module 1002 determines the application with the highest recommendation probability among all recommended application types as the recommended application.

[0247] (Optional) Step 5(d): The application recommendation module 1002 determines whether the number of recommended applications is less than the number of icons that the application suggestion card can hold.

[0248] In this embodiment, step 5(d) is an optional step. If the application recommendation module 1002 determines that the number of recommended applications is less than the number of icons that the application suggestion card can hold, then the application recommendation module 1002 can execute step 5(e).

[0249] (Optional) Step 5(e): If the number of recommended applications is less than the number of icons that the application suggestion card can hold, the application recommendation module 1002 will identify one or more applications that have not yet been identified as recommended applications as recommended applications, so that the number of recommended applications is equal to the number of icons that the application suggestion card can hold.

[0250] For a description of the process of the application recommendation module 1002 executing step 5(e), please refer to the relevant description in the foregoing embodiments, which will not be repeated here.

[0251] Since an application can belong to one or more application types, the recommended applications determined by the application recommendation module 1002 may include multiple duplicate applications. Therefore, in some implementations, after performing the aforementioned steps 5(c) and / or 5(e), the application recommendation module 1002 can deduplicate the recommended applications, removing duplicate applications to obtain the final recommended applications.

[0252] In some implementations, after executing step 5, the application recommendation module 1002 can send a request to the server, requesting the server to send it server-recommended applications. The server can be a server provided by the manufacturer of the electronic device 100, a server provided by a platform providing application download services, etc. The server-recommended applications include one or more applications determined by the server. After receiving the server-recommended applications sent by the server, the application recommendation module 1002 can determine the server-recommended applications as recommended applications, or modify the previously determined recommended applications based on the server-recommended applications.

[0253] Step 6: The application recommendation module 1002 sends the recommended applications to the display module 1003.

[0254] Step 7: The display module 1003 generates and displays an application suggestion card based on the above recommended applications.

[0255] An app suggestion card is a control that can be used to display icons of recommended apps. The form of an app suggestion card can be related to its specifications and status. For a description of the relationship between the form of an app suggestion card and its specifications and status, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0256] The display module 1003 can generate application suggestion cards based on the recommended applications sent by the application recommendation module 1002. The application suggestion cards may include icons of one or more of the recommended applications. The display module 1003 can then display the generated application suggestion cards.

[0257] This application also provides a computer program product that, when run on an electronic device, causes the electronic device to perform the method in any of the foregoing embodiments.

[0258] This application also provides a computer-readable storage medium storing computer program code, which, when executed by an electronic device, causes the electronic device to perform the method in any of the foregoing embodiments.

[0259] The computer program product and computer-readable storage medium provided in this application embodiment are used to execute the application permission management method described above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0260] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0261] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0262] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0263] In summary, the above description is merely an embodiment of the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made according to the disclosure of the present invention should be included within the scope of protection of the present invention.

Claims

1. An application recommendation method characterized by comprising: The method is applied to an electronic device, and the method comprises: The electronic device acquires usage data and state data of a plurality of applications, the usage data and the state data being used by the electronic device to determine recommendation probabilities of the applications, the recommendation probabilities indicating probabilities of the applications being used by a user in states indicated by the state data; The electronic device determines a first application quantity according to the usage data of the plurality of applications, the first application quantity being an average number of applications enabled by the electronic device per unit time; If the first application quantity is less than a first value, the electronic device determines the recommendation probabilities of the plurality of applications by using the usage data and the state data of the plurality of applications to obtain enabling probabilities of the plurality of applications in a historical time; If the first application quantity is greater than or equal to the first value and less than a second value, the electronic device determines the recommendation probabilities of the plurality of applications by inputting the usage data and the state data of the plurality of applications into a first decision tree; the first decision tree is a decision tree trained by the electronic device using the usage data of the plurality of applications as input and using enabling probabilities of the plurality of applications in a historical time obtained according to the usage data of the plurality of applications as output; If the first application quantity is greater than or equal to the second value, the electronic device determines the recommendation probabilities of the plurality of applications by processing the usage data and the state data of the plurality of applications according to a multi-path recall algorithm; The electronic device determines a plurality of recommended applications from the plurality of applications, the plurality of recommended applications comprising applications with maximum recommendation probabilities in each of a plurality of application types. The electronic device displays an application suggestion card, and icons of the plurality of recommended applications are displayed in the application suggestion card. Upon receiving a user operation on a first icon in the icons of the plurality of recommended applications, the electronic device starts a recommended application corresponding to the first icon.

2. The method of claim 1, wherein, The plurality of application types comprises: After determining the sum of the recommendation probabilities of the applications included in each of the application types included in the electronic device, the plurality of application types with the top recommendation probabilities.

3. The method of claim 1, wherein, The plurality of application types comprises: After determining the number of applications included in each of the application types included in the electronic device, the plurality of application types with the top application numbers.

4. The method of claim 1, wherein The number of the plurality of application types is the minimum value of the number of application types used by the electronic device in a historical time and the maximum number of icons that can be accommodated by the application recommendation card.

5. The method of claim 1, wherein, The plurality of application types comprises: After determining the sum of the recommendation probabilities of the applications included in each of the application types included in the electronic device, the plurality of application types with the sum of the recommendation probabilities greater than a threshold value.

6. The method of claim 1, wherein, The number of applications with maximum recommendation probabilities in each of the plurality of application types is less than the maximum number of icons that can be accommodated by the application recommendation card. The one or more applications are one or more applications in the plurality of applications other than the application with the maximum recommendation probability in each of the plurality of application types. The sum of the application with the maximum recommendation probability in each of the plurality of application types and the number of the one or more applications is equal to the maximum number of icons that can be accommodated by the application recommendation card.

7. The method of claim 6, wherein, The one or more applications are one or more applications in the plurality of applications other than the application with the maximum recommendation probability in each of the plurality of application types. Alternatively, The one or more applications are the applications with the maximum recommendation probability in each of the partial application types in the plurality of applications other than the application with the maximum recommendation probability in each of the plurality of application types.

8. The method of any one of claims 1-7, wherein, The application recommendation card includes a plurality of regions, and icons of recommended applications of different application types are displayed in different regions. Alternatively, the icons of recommended applications of different application types have different border colors. Alternatively, the icons of recommended applications of different application types are located in regions with different background patterns.

9. The method according to any one of claims 1 to 7, characterized in that, The electronic device displays an application recommendation card, and the method includes: The electronic device displays part of the recommended applications in the application recommendation card. The electronic device receives a first operation for expanding the application recommendation card. The electronic device displays more of the recommended applications in the application recommendation card than the part of the recommended applications.

10. An electronic device, comprising: The electronic device includes a memory and one or more processors, the memory is coupled to the one or more processors, the memory stores computer program code including computer instructions, and the one or more processors invoke the computer instructions to cause the electronic device to perform the method of any one of claims 1-9.

11. A computer program product comprising instructions, characterized in that, The computer program product, when running on an electronic device, causes the electronic device to perform the method of any one of claims 1-9.

12. A computer-readable storage medium comprising instructions, wherein: When the instructions run on an electronic device, the electronic device performs the method of any one of claims 1-9.

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