An application recommendation method and apparatus

Abstract

The application relates to an application program recommendation method and device, which comprises the following steps: obtaining a first list and a second list generated in advance in a terminal device; determining a time parameter of an application program in the first list based on a latest use time of the application program; determining a scene urgency parameter of an application program in the second list based on a current scene parameter obtained in advance and a latest recommendation time of the application program; determining a priority value of each application program in the first list and the second list based on the time parameter and / or the scene urgency parameter; and determining a to-be-recommended application program from the first list and the second list according to the priority value. According to the application, the priority value of the application program is determined according to the time parameter and the scene urgency parameter, information in the time dimension and the scene dimension is comprehensively used, the recommended application program can be more consistent with the use intention and the use requirement of a user, and the use rate of the recommended application program is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more particularly to an application recommendation method and apparatus. Background Technology

[0002] With the development of technologies such as communication and chips, automobiles have gradually evolved from a means of transportation into a "third space" that integrates multiple scenarios such as travel, entertainment, and office work, following the office and residence.

[0003] In in-vehicle intelligent systems, users can use various types of applications on the in-vehicle display screen to enjoy music, watch videos, listen to e-books, make phone calls, receive traffic alerts, and more. To improve ease of use, the in-vehicle intelligent system will recommend applications that may suit the user's current needs on the in-vehicle display screen.

[0004] For example, in-vehicle intelligent systems typically recommend applications based on the user's past usage, or on vehicle information and user behavior data. However, applications recommended using these methods still fail to accurately match the user's preferences and needs, resulting in low usage rates. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, a first aspect of the present invention proposes an application recommendation method, the method comprising:

[0006] Obtain a first list and a second list pre-generated in the terminal device; the first list includes applications used within a preset time period before the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period;

[0007] Based on the most recent usage time of the applications in the first list, determine the time parameters of the applications in the first list;

[0008] Based on the pre-acquired current scenario parameters and the most recent recommendation time of the applications in the second list, the scenario urgency parameters of the applications in the second list are determined;

[0009] Based on the time parameter and / or the scenario urgency parameter, determine the priority value of each application in the first list and the second list;

[0010] The applications to be recommended are determined from the first list and the second list based on the priority value.

[0011] Optionally, the applications in the first list are sorted in ascending or descending order according to the difference between the most recent usage time and the current time. Determining the temporal parameters of the applications in the first list based on their most recent usage time includes:

[0012] Based on the recent usage time of the applications in the first list, determine the time interval between the recent usage times of two adjacent applications;

[0013] According to the preset time segment division rules, the time segment to which the time interval belongs is determined, and the interval coefficient is determined according to the time segment;

[0014] Based on the most recent usage time and the interval coefficient, the time parameters of the applications in the first list are determined.

[0015] Optionally, determining the temporal parameters of applications in the first list based on the most recent usage time and the interval coefficient includes:

[0016] The first product is obtained by multiplying the most recent usage time of each application in the first list with a preset first weighting coefficient;

[0017] The sum of the first product and the interval coefficient is determined to obtain the time parameters of the applications in the first list.

[0018] Optionally, before determining the scenario urgency parameters of the applications in the second list based on pre-acquired current scenario parameters and the recent usage time of the applications in the second list, the method further includes:

[0019] Obtain the list of applications currently in use by the terminal device and the current hardware status data of the terminal device to obtain the terminal device status data;

[0020] Acquire the user's current vital signs data and the external environmental data surrounding the terminal device;

[0021] The current scene parameters are determined based on the terminal device status data, the vital signs data, and the external environment data.

[0022] Optionally, determining the scenario urgency parameters of the applications in the second list based on pre-acquired current scenario parameters and the most recent recommendation time of the applications in the second list includes:

[0023] Based on the pre-acquired current scenario parameters, determine the scenario urgency level of the applications in the second list at the current moment;

[0024] The second product is obtained by multiplying the most recent recommendation time of each application in the second list with the second weighting coefficient;

[0025] The sum of the urgency level and the second product is determined to obtain the scenario urgency parameters of the applications in the second list.

[0026] Optionally, determining the priority value of each application in the first list and the second list based on the time parameter and / or the scenario urgency parameter includes:

[0027] If the target application is in both the first list and the second list, the smaller value between the time parameter and the scenario urgency parameter is obtained, and the smaller value is used as the priority value of the target application.

[0028] If the target application is only in the first list, then the time parameter is used as the priority value of the target application;

[0029] If the target application is only in the second list, then the scenario urgency parameter is used as the priority value of the target application.

[0030] A second aspect of the present invention provides an application recommendation device, the device comprising:

[0031] The list acquisition module is used to acquire a first list and a second list pre-generated in the terminal device; the first list includes applications used within a preset time period before the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period;

[0032] A time parameter determination module is used to determine the time parameters of the applications in the first list based on their most recent usage time.

[0033] The urgency parameter determination module is used to determine the scenario urgency parameters of the applications in the second list based on the pre-acquired current scenario parameters and the most recent recommendation time of the applications in the second list;

[0034] The priority value determination module is used to determine the priority value of each application in the first list and the second list based on the time parameter and / or the scenario urgency parameter.

[0035] The application to be recommended module is used to determine the application to be recommended from the first list and the second list according to the priority value.

[0036] Optionally, the applications in the first list are sorted in ascending or descending order according to the difference between the most recent usage time and the current time, and the time parameter determination module is specifically used for:

[0037] Based on the recent usage time of the applications in the first list, determine the time interval between the recent usage times of two adjacent applications;

[0038] According to the preset time segment division rules, the time segment to which the time interval belongs is determined, and the interval coefficient is determined according to the time segment;

[0039] Based on the most recent usage time and the interval coefficient, the time parameters of the applications in the first list are determined.

[0040] Optionally, the timing parameter determination module is further configured to:

[0041] The first product is obtained by multiplying the most recent usage time of each application in the first list with a preset first weighting coefficient;

[0042] The sum of the first product and the interval coefficient is determined to obtain the time parameters of the applications in the first list.

[0043] Optionally, before determining the scenario urgency parameters of the applications in the second list based on pre-acquired current scenario parameters and the recent usage time of the applications in the second list, the method further includes:

[0044] Obtain the list of applications currently in use by the terminal device and the current hardware status data of the terminal device to obtain the terminal device status data;

[0045] Acquire the user's current vital signs data and the external environmental data surrounding the terminal device;

[0046] The current scene parameters are determined based on the terminal device status data, the vital signs data, and the external environment data.

[0047] Optionally, the scenario urgency parameter determination module is used for:

[0048] Based on the pre-acquired current scenario parameters, determine the scenario urgency level of the applications in the second list at the current moment;

[0049] The second product is obtained by multiplying the most recent recommendation time of each application in the second list with the second weighting coefficient;

[0050] The sum of the urgency level and the second product is determined to obtain the scenario urgency parameters of the applications in the second list.

[0051] Optionally, the priority value determination module is specifically used for:

[0052] If the target application is in both the first list and the second list, the smaller value between the time parameter and the scenario urgency parameter is obtained, and the smaller value is used as the priority value of the target application.

[0053] If the target application is only in the first list, then the time parameter is used as the priority value of the target application;

[0054] If the target application is only in the second list, then the scenario urgency parameter is used as the priority value of the target application.

[0055] A third aspect of the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the application recommendation method as described in the first aspect.

[0056] A fourth aspect of the present invention provides a computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the application recommendation method as described in the first aspect.

[0057] The embodiments of the present invention have the following beneficial effects:

[0058] The application recommendation method provided in this invention obtains a first list and a second list pre-generated in a terminal device. The first list includes applications used within a preset time period prior to the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period. Based on the most recent usage time of the applications in the first list, a time-related parameter is determined for each application. Based on pre-obtained current scenario parameters and the most recent recommendation time of the applications in the second list, a scenario urgency parameter is determined for each application in the second list. Based on the time-related parameter and / or the scenario urgency parameter, a priority value is determined for each application in the first and second lists. An application to be recommended is then determined from the first and second lists based on the priority value. This solution determines both the time-related parameter and the scenario urgency parameter of the application, and determines the priority value of the application based on these parameters. By comprehensively using information from both the time and scenario dimensions, the recommended applications can better match the user's usage intentions and needs, thereby improving the usage rate of the recommended applications.

[0059] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0061] Figure 1 A flowchart illustrating the steps of a first application recommendation method provided in an embodiment of the present invention;

[0062] Figure 2 This is a flowchart of the steps of the second vehicle application recommendation method provided in the embodiments of the present invention;

[0063] Figure 3 This is a structural block diagram of an application recommendation device provided in an embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] This specification provides the operational steps for the methods described in the embodiments or flowcharts, but may include more or fewer operational steps based on conventional or non-inventive labor. In actual system or server product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0066] Figure 1 A flowchart illustrating the steps of a first application recommendation method provided in an embodiment of the present invention. The method may include the following steps:

[0067] Step 101: Obtain a first list and a second list pre-generated in the terminal device; the first list includes applications used within a preset time period before the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period.

[0068] The execution subject of this invention embodiment is a terminal device, which may include an in-vehicle intelligent system, a mobile terminal, a computer, a tablet computer, a wearable intelligent device, etc.

[0069] In-vehicle intelligent systems are operating systems used in cars and belong to automotive software. Specifically, they can be Android systems, iOS systems, etc.

[0070] The terminal device obtains the applications used by the user within a preset time period before the current time, arranges these applications in chronological order, and obtains the first list.

[0071] Furthermore, the system retrieves applications recommended by a preset recommendation algorithm within a preset time period to obtain a second list.

[0072] The preset recommendation algorithm can be any existing recommendation algorithm, such as one based on vehicle condition information, one based on user behavior information, or one based on cloud data. The preset algorithm primarily uses a weighted ranking method, where a higher weight value results in a higher ranking. One or more of the top-ranked applications are then selected as recommended applications. The weight value is obtained by accumulating defined values ​​from user behavior information, vehicle condition information, or other specific information. These parameters typically include click frequency, vehicle speed, and application usage duration.

[0073] Step 102: Determine the time parameters of the applications in the first list based on their most recent usage time.

[0074] Recent usage time refers to the time elapsed since the application in the first list was used. For example, if the application currently being used is the air conditioning application, and the music application was used 5 minutes ago, and the map application was used 1 hour ago, then the recent usage time for the air conditioning application would be 0, the recent usage time for the music application would be 5 minutes, and the recent usage time for the map application would be 1 hour.

[0075] The recent usage time of an application can reflect the likelihood of a user using the application again; the more recent the usage time, the greater the likelihood of using the application again.

[0076] Therefore, the application's time parameters can be determined based on the most recent usage time.

[0077] Step 103: Based on the pre-acquired current scene parameters and the most recent recommendation time of the applications in the second list, determine the scene urgency parameters of the applications in the second list.

[0078] Current scenario parameters can include terminal device status data, vehicle condition data, user's vital signs data, and external environment data. The application's most recent recommendation time reflects the likelihood of the user using the application again.

[0079] Based on the current scenario parameters and the most recent recommendation time, the urgency of the application in relation to the current scenario can be determined, thus obtaining the scenario urgency parameters of the application.

[0080] Step 104: Based on the time parameter and / or the scenario urgency parameter, determine the priority value of each application in the first list and the second list.

[0081] An application may exist in both the first and second lists. The application's time-related parameters and scenario urgency parameters can be calculated through the aforementioned steps. By obtaining the application's time-related parameters and scenario urgency parameters and performing appropriate calculations, the application's priority value can be obtained.

[0082] For applications that exist only in the first list, the time parameters can be obtained through the aforementioned steps, and the priority value of the application can be determined based on the time parameters.

[0083] For applications that only exist in the second list, the scenario urgency parameter can be obtained through the aforementioned steps, and the priority value of the application can be determined based on the scenario urgency parameter.

[0084] This allows us to obtain the priority values ​​for each application in the first and second lists.

[0085] Step 105: Determine the applications to be recommended from the first list and the second list according to the priority values.

[0086] After obtaining the priority values ​​of each application, the priority values ​​can be sorted in descending order, and the top N applications (N is a natural number) can be selected as recommended applications. Then, these recommended applications are displayed on the in-vehicle display screen in priority order, allowing users to easily select the application they need.

[0087] In summary, the application recommendation method provided by this invention obtains a first list and a second list pre-generated in a terminal device. The first list includes applications used within a preset time period prior to the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period. Based on the most recent usage time of the applications in the first list, a time-related parameter is determined for each application. Based on pre-obtained current scenario parameters and the most recent recommendation time of the applications in the second list, a scenario urgency parameter is determined for each application in the second list. Based on the time-related parameter and / or the scenario urgency parameter, a priority value is determined for each application in the first and second lists. The application to be recommended is then determined from the first and second lists based on the priority value. This solution determines both the time-related parameter and the scenario urgency parameter of the application, and determines the priority value of the application based on these parameters. By comprehensively utilizing information from both the time and scenario dimensions, the recommended applications can better match the user's usage intentions and needs, thereby improving the usage rate of the recommended applications.

[0088] Figure 2 A flowchart illustrating the steps of a second application recommendation method provided in an embodiment of the present invention. The method may include the following steps:

[0089] Step 201: Obtain a first list and a second list pre-generated in the terminal device; the first list includes applications used within a preset time period before the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period.

[0090] In this embodiment of the invention, step 201 can refer to step 101, and will not be repeated here.

[0091] Step 202: Determine the time interval between the recent usage times of two adjacent applications based on the recent usage times of the applications in the first list; the applications in the first list are arranged in ascending or descending order according to the difference between the recent usage time and the current time.

[0092] Determine the difference between the most recent usage time and the current time for each application in the first list, and then sort the applications in ascending or descending order according to the difference. This way, adjacent applications are considered applications used by the user previously.

[0093] The difference between the most recent usage times of two adjacent applications is the interval between when a user uses these two applications.

[0094] This interval characterizes the coupling relationship between two applications. A longer interval indicates a weaker coupling, while a shorter interval indicates a stronger coupling, suggesting they may be related applications. Weaker coupling allows for the addition of other applications.

[0095] Specifically, the time interval between the most recent usage times of two adjacent applications can be either the forward time interval between the current application and the previous application, or the backward time interval between the current application and the next application.

[0096] Of course, all applications in the first list must use either the forward time interval or the backward time interval; they cannot be mixed.

[0097] Step 203: Determine the time segment to which the time interval belongs according to the preset time segment division rules, and determine the interval coefficient according to the time segment.

[0098] The time segment division rules can be preset according to the actual interval time. For example, the interval time can be divided into four segments of 5 minutes, 15 minutes, and 60 minutes, that is, [0,5] is one segment, (5,15] is one segment, (15,60] is one segment, and (60,+∞] is one segment.

[0099] The interval coefficients are set according to the segment order. For example, if four segments are arranged in ascending order of interval time, the interval coefficients are 4, 3, 2, and 1 respectively. The interval coefficient is inversely proportional to the interval duration.

[0100] Step 204: Determine the time parameters of the applications in the first list based on the most recent usage time and the interval coefficient.

[0101] The time parameter can be obtained by mathematically processing the most recent usage time and the interval coefficient. Specifically, the time parameter can be the sum of the two, or a weighted sum of the two.

[0102] In one possible implementation, step 204 includes steps 2041-2042:

[0103] Step 2041: Determine the product of the most recent usage time of each application in the first list and a preset first weighting coefficient to obtain the first product;

[0104] Step 2042: Determine the sum of the first product and the interval coefficient to obtain the time parameters of the applications in the first list.

[0105] In steps 2041-2042, the timing parameters can be calculated using the following formula:

[0106] Ts=Nt*w1+It (1)

[0107] Where Ts represents the time parameter, Nt represents the most recent usage time, It represents the interval coefficient, w1 is the first weighting coefficient, which can take a value of 10, and the weighting coefficient of It can be considered as 1. The unit of the most recent usage time mentioned above is seconds.

[0108] For example, if an application's most recent usage time is 5 minutes and its interval coefficient is 4, then the application's time parameter Ts = 5 * 60 * 10 + 4 = 3004.

[0109] Step 205: Obtain the list of applications currently in use by the terminal device and the current hardware status data of the terminal device to obtain the terminal device status data.

[0110] Step 206: Obtain the user's current vital signs data and the external environment data around the terminal device;

[0111] Step 207: Determine the current scene parameters based on the terminal device status data, the vital signs data, and the external environment data.

[0112] Steps 205-207 describe the method for collecting current scene parameters. Specifically, terminal device status data can be collected, including obtaining a list of applications currently used by the terminal device and the current hardware status of the terminal device.

[0113] In addition, it collects the user's current vital signs data, such as the user's breathing, body temperature, pulse, blood pressure, etc., to determine the user's current emotional state and health status.

[0114] In addition, collecting external environmental data, including weather, temperature, natural environment, and geographical location, can significantly impact user experience with the application. For example, users are more likely to use an air conditioning app in hot weather.

[0115] Based on the current scenario parameters, we can determine the applications that the current user is likely to use, and recommend these applications to the user to improve the user experience.

[0116] Step 208: Determine the scenario urgency level of the applications in the second list at the current moment based on the pre-acquired current scenario parameters.

[0117] The scenario urgency level indicates the degree of urgency for triggering a recommendation behavior, and can be calculated using a segmentation function. The segmentation function mainly divides into five levels: very weak urgency, weaker urgency, moderate urgency, stronger urgency, and very strong urgency. These five levels are represented by an urgency coefficient. It can be understood that the urgency coefficient can be set according to the magnitude of the time-related parameters.

[0118] For example, when the time unit is seconds, the urgency coefficients for the above five levels are 3005, 3004, 3003, 3002, and 3001, respectively. When the time unit is minutes, the urgency coefficients for the above five levels can be 3000 / 60+5, 3000 / 60+4, 3000 / 60+3, and 3000 / 60+1.

[0119] The greater the urgency, the smaller the value; more urgent the action, the stronger its timeliness. The strength of timeliness provides a direct basis for the ranking of recommended applications.

[0120] For example, scenarios with very low urgency include listening to e-books; scenarios with relatively low urgency include air conditioning reminders; scenarios with moderate urgency include making phone calls; scenarios with relatively high urgency include traffic warnings; and scenarios with very high urgency include vehicle malfunctions.

[0121] Step 209: Determine the product of the most recent recommendation time and the second weighting coefficient for each application in the second list to obtain the second product;

[0122] Step 210: Determine the sum of the urgency level and the second product to obtain the scenario urgency parameters of the applications in the second list.

[0123] In steps 209-210, the formula for the scenario urgency parameter is:

[0124] Is=Ni*w2+Si (2)

[0125] Where Is represents the scenario urgency parameter, Ni represents the most recent recommendation time of the application in the second list, w2 represents the second weighting coefficient, and Si represents the urgency level.

[0126] W2 is the second weighting coefficient, which can be 10. The weighting coefficient of Si can be considered as 1. The unit of the most recent recommendation time mentioned above is seconds.

[0127] For example, if an application's most recent recommendation time is 5 minutes and its urgency level is 3004, then the application's scenario urgency parameter Ts = 5 * 60 * 10 + 3004 = 6004.

[0128] Explanation of the urgency coefficient value: Compared to the interval coefficient, the urgency coefficient is increased by 3000. That is, if the most recent recommendation time is 5 minutes, then Nt*w1 equals 300 seconds multiplied by 10. The purpose is to ensure that applications used in the last 5 minutes are always ranked first, ensuring user convenience. The urgency of recommended applications is considered only after excluding these last 5 minutes of activity.

[0129] Step 211: If the target application is in both the first list and the second list, then obtain the smaller value between the time parameter and the scenario urgency parameter, and use the smaller value as the priority value of the target application.

[0130] For a target application that is simultaneously in the first list and the second list, the time parameter and scenario urgency parameter of the target application can be calculated through the above steps, and the smaller value of the two can be taken as the priority value of the target application, that is, priority value level P = min{Ts, Is}.

[0131] For example, if the time parameter of the target application is 3004 and the urgency parameter of the scenario is 6004, then the priority value of the target application is 3004.

[0132] To illustrate the implementation results, based on the recent application sorting, applications with higher urgency will be added to the recent application list among applications with weaker coupling relationships. Applications with weaker coupling relationships can be those with an interval of more than 5 minutes. In the case where there are no recently used applications, urgent applications will be added to the top of the recent recommended application list. The case where there are no recently used applications can be considered as the case where the recent operation time is more than 5 minutes.

[0133] Step 212: If the target application is only in the first list, then the time parameter is used as the priority value of the target application.

[0134] If the target application is only in the first list, only the time parameter can be calculated. Its scenario urgency parameter is considered to be infinite, and its time parameter is taken as the priority value of the target application.

[0135] Step 213: If the target application is only in the second list, then the scenario urgency parameter is used as the priority value of the target application.

[0136] If the target application is only in the second list, only the scenario urgency parameter can be calculated. Its time parameter is considered to be infinite, and the scenario urgency parameter is taken as the priority value of the target application.

[0137] Step 214: Determine the applications to be recommended from the first list and the second list according to the priority value.

[0138] In this embodiment of the invention, step 214 can refer to step 105, and will not be repeated here.

[0139] In conclusion, Figure 2 The application recommendation method provided in [the document], in addition to having [the following features], Figure 1 The beneficial effects of the application recommendation method in this paper are further demonstrated by the new definition of two variables in the ranking algorithm: "time parameter" and "scenario urgency parameter". The "time parameter" creatively utilizes the interval between different applications in the first list as the input parameter for ranking. The "urgency parameter" creatively utilizes the user's usage urgency level classification based on the scenario as the input parameter for ranking. This allows for better use of information about the time dimension and urgency level to better meet the needs of users.

[0140] Figure 3 A structural block diagram of an application recommendation device provided in an embodiment of the present invention. The device 300 includes:

[0141] The list acquisition module 301 is used to acquire a first list and a second list pre-generated in the terminal device; the first list includes applications used within a preset time period before the current time, and the second list includes applications recommended according to a preset recommendation algorithm within the preset time period;

[0142] The time parameter determination module 302 is used to determine the time parameters of the applications in the first list based on the most recent usage time of the applications in the first list.

[0143] The urgency parameter determination module 303 is used to determine the scenario urgency parameters of the applications in the second list based on the pre-acquired current scenario parameters and the most recent recommendation time of the applications in the second list;

[0144] Priority value determination module 304 is used to determine the priority value of each application in the first list and the second list based on the time parameter and / or the scenario urgency parameter;

[0145] The application to be recommended determination module 305 is used to determine the application to be recommended from the first list and the second list according to the priority value.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] In another embodiment of the present invention, a device is also provided, the device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the application recommendation method described in the embodiment of the present invention.

[0148] In another embodiment of the present invention, a computer-readable storage medium is also provided, wherein at least one instruction, at least one program, code set, or instruction set is stored in the storage medium, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the application recommendation method described in the embodiments of the present invention.

[0149] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention 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 (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access 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 disk (SSD)).

[0150] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0151] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0152] The above description is merely a preferred embodiment 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 within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. An application program recommendation method characterized by comprising: The method comprises: obtaining a first list and a second list generated in advance in a terminal device; the first list comprises application programs used in a preset time period before a current time, and the second list comprises application programs recommended in the preset time period according to a preset recommendation algorithm; determining a time parameter of the application programs in the first list based on the latest use time of the application programs in the first list; determining a scene urgency parameter of the application programs in the second list based on a current scene parameter obtained in advance and the latest recommendation time of the application programs in the second list; determining a priority value of each of the application programs in the first list and the second list based on the time parameter and / or the scene urgency parameter; determining a to-be-recommended application program from the first list and the second list according to the priority value; the application programs in the first list are arranged in ascending order or descending order according to the difference between the latest use time and the current time, and the time parameter of the application programs in the first list is determined based on the latest use time of the application programs in the first list, comprising: determining a time interval between the latest use time of two adjacent application programs according to the latest use time of the application programs in the first list; determining a time section to which the time interval belongs according to a preset time section division rule, and determining an interval coefficient according to the time section; determining the time parameter of the application programs in the first list based on the latest use time and the interval coefficient.

2. The method of claim 1, wherein, The time parameter of the application programs in the first list is determined based on the latest use time and the interval coefficient, comprising: determining a first product by multiplying the latest use time of each application program in the first list by a preset first weighting coefficient; determining the sum of the first product and the interval coefficient to obtain the time parameter of the application programs in the first list.

3. The method of claim 1, wherein, Before determining the scene urgency parameter of the application programs in the second list based on the current scene parameter obtained in advance and the latest use time of the application programs in the second list, the method further comprises: obtaining a list of application programs currently used by the terminal device, hardware state data of the terminal device to obtain terminal device state data; obtaining current physical data of a user of the terminal device and external environment data around the terminal device; determining the current scene parameter according to the terminal device state data, the physical data and the external environment data.

4. The method of claim 1, wherein, The scene urgency parameter of the application programs in the second list is determined based on the current scene parameter obtained in advance and the latest recommendation time of the application programs in the second list, comprising: determining a scene urgency level of the application programs in the second list at the current time according to the current scene parameter obtained in advance; determining a second product by multiplying the latest recommendation time of each application program in the second list by a second weighting coefficient; determining a scene urgency parameter of the application program in the second list by summing the urgency level and the second product.

5. The method of claim 1, wherein, The priority value of each of the application programs in the first list and the second list is determined based on the time parameter and / or the scene urgency parameter, including: if the target application program is in both the first list and the second list, obtaining a smaller value between the time parameter and the scene urgency parameter, and taking the smaller value as the priority value of the target application program; if the target application program is only in the first list, taking the time parameter as the priority value of the target application program; if the target application program is only in the second list, taking the scene urgency parameter as the priority value of the target application program.

6. An application program recommendation device characterized by comprising: The device comprises: a list obtaining module configured to obtain a first list and a second list generated in advance in a terminal device, the first list comprising application programs used in a preset time period before a current time, and the second list comprising application programs recommended in the preset time period according to a preset recommendation algorithm; a time parameter determining module configured to determine a time parameter of the application programs in the first list based on a latest use time of the application programs in the first list; an urgency parameter determining module configured to determine a scene urgency parameter of the application programs in the second list based on a current scene parameter obtained in advance and a latest recommendation time of the application programs in the second list; a priority value determining module configured to determine a priority value of each of the application programs in the first list and the second list based on the time parameter and / or the scene urgency parameter; a to-be-recommended application determining module configured to determine a to-be-recommended application program from the first list and the second list according to the priority value. The application programs in the first list are arranged in ascending or descending order of a difference between the latest use time and the current time, and the time parameter determining module is specifically configured to: determine a time interval between the latest use times of two adjacent application programs according to the latest use time of the application programs in the first list; determine a time section to which the time interval belongs according to a preset time section division rule, and determine an interval coefficient according to the time section; determine the time parameter of the application programs in the first list based on the latest use time and the interval coefficient.

7. An electronic device, comprising: The electronic device comprises a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the application program recommendation method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the application program recommendation method according to any one of claims 1-5.

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