Content recommendation method and device, electronic equipment and medium

Abstract

The invention provides a content recommendation method and device, electronic equipment and a medium, and relates to the technical field of computers, in particular to the technical field of natural language processing, large models and intelligent recommendation. According to the implementation scheme, preference characteristics of a user are obtained, the preference characteristics indicate the tendency degree of the user for obtaining associated content of a hot topic in a first time period, and the occurrence time of an associated event corresponding to the associated content falls into the first time period; obtaining target data associated with the hot topic at the current moment; processing the target data by using a large model to determine at least one current hot topic; and according to the preference characteristics and the at least one current hot topic, determining recommended content for the user.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the fields of natural language processing, large models and intelligent recommendation technologies, and specifically to a content recommendation method, device, electronic device, computer-readable storage medium and computer program product. Background Art

[0002] Currently, the platform determines current hot topics through manual editing in order to push related content of the current hot topics to all users.

[0003] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a content recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of the present disclosure, a content recommendation method is provided, comprising: obtaining a user's preference characteristics, wherein the preference characteristics indicate the user's tendency to obtain associated content of a hot topic within a first time period, and the occurrence time of an associated event corresponding to the associated content falls within the first time period; obtaining target data associated with the hot topic at the current moment; processing the target data using a large model to determine at least one current hot topic; and determining recommended content for the user based on the preference characteristics and the at least one current hot topic.

[0006] According to another aspect of the present disclosure, a content recommendation device is provided, comprising: a first module configured to obtain a user's preference characteristics, wherein the preference characteristics indicate the user's tendency to obtain related content of a hot topic within a first time period, and the occurrence time of an associated event corresponding to the related content falls within the first time period; a second module configured to obtain target data associated with the hot topic at the current moment; a third module configured to process the target data using a large model to determine at least one current hot topic; and a fourth module configured to determine recommended content for the user based on the preference characteristics and the at least one current hot topic.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above method.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the above method when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, a content recommendation method is provided, which determines at least one current hot topic at the current moment by processing target data using a large model, and determines whether to recommend relevant content of the current hot topic to the user based on the user's tendency to obtain related content of the current hot topic in a timely manner (for example, whether the user tends to quickly obtain related content within a short period of time after an associated event of the related content of the hot topic occurs). In this way, the current hot topic can be identified in a timely and accurate manner and relevant content can be recommended to interested users in a targeted manner, thereby improving the user experience.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0013] Figure 1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented, according to an exemplary embodiment;

[0014] Figure 2 A flowchart of a content recommendation method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown;

[0017] Figure 5 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown;

[0019] Figure 7 shows a structural block diagram of a content-based recommendation device according to an embodiment of the present disclosure; and

[0020] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0021] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0022] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0023] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0024] In related technologies, the platform determines the current hot topics through manual editing in order to push related content of the current hot topics to all users.

[0025] To solve the above problems, the present disclosure provides a content recommendation method, which determines at least one current hot topic at the current moment by using a large model to process target data, and determines whether to recommend relevant content of the current hot topic to the user based on the user's tendency to obtain related content of the current hot topic in a timely manner (for example, whether the user tends to quickly obtain related content within a short period of time after the occurrence of an associated event of the related content of the hot topic). In this way, the current hot topic can be identified in a timely and accurate manner and relevant content can be recommended to interested users in a targeted manner, thereby improving the user experience.

[0026] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0028] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the content recommendation method to be performed.

[0029] In some embodiments, server 120 may also provide other services or software applications that may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0030] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is an example of a system for implementing the content recommendation method described herein and is not intended to be limiting.

[0031] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to perform the content recommendation method. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0032] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0033] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0034] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0035] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0036] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0037] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0038] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store target data and other information. For example, one or more of the databases 130 may be used to store information such as text files and video files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0039] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0040] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0041] Figure 2 A flowchart of a content recommendation method according to an embodiment of the present disclosure is shown.

[0042] like Figure 2 As shown, the content recommendation method 200 includes:

[0043] Step 210: Obtain a user preference feature, wherein the preference feature indicates the user's tendency to obtain related content of the hot topic within a first time period, and the occurrence time of the related event corresponding to the related content falls within the first time period;

[0044] Step 220: Obtain target data related to the current hot topic;

[0045] Step 230: Process the target data using the large model to determine at least one current hot topic; and

[0046] Step 240: Determine recommended content for the user based on the preference characteristics and at least one current hot topic.

[0047] Therefore, by using a large model to process the target data, at least one current hot topic at the current moment is determined, and based on the user's tendency to obtain related content of the current hot topic in a timely manner (for example, whether the user tends to quickly obtain related content within a short period of time after the related event of the related content of the hot topic occurs), it is determined whether to recommend related content of the current hot topic to the user. In this way, the current hot topic can be identified in a timely and accurate manner, and related content can be recommended to interested users in a targeted manner, thereby improving the user experience.

[0048] In step 210, the first time period can be, for example, 5 minutes, 30 minutes, 1 hour 5 hours, or 1 day, so that the user's preference for timely obtaining content related to the hot topic in a relatively short period of time can be determined based on the first time period. For example, the time when an event related to the content related to the hot topic occurs can be used as the starting point of the first time period.

[0049] In step 210 , illustratively, the occurrence time of the associated event may be, for example, the occurrence time of a specific event associated with the relevant content of the hot topic, or the time when the hot topic appears on a relevant list.

[0050] In step 220 , the target data may be, for example, a list of hot topics on the platform or a collection of posts with high discussion (eg, high number of views and comments) in social media.

[0051] In step 230, exemplarily, the target data includes text data, and the big model may be, for example, a big language model, so that the big language model can be used to perform semantic recognition on the target data, thereby accurately and efficiently determining at least one current hot topic; exemplarily, the target data may further include at least one of audio data, image data, and video data, and the big model may be, for example, a multimodal big model, so that the multimodal big model can be used to process multiple types of data simultaneously to improve the accuracy of determining at least one current hot topic.

[0052] In step 240, exemplarily, in response to determining based on the user's preference characteristics that the user has a high tendency to obtain related content of hot topics within the first time period, relevant content of all current hot topics is recommended to the user; exemplarily, in response to determining based on the user's preference characteristics that the user has a low tendency to obtain related content of hot topics within the first time period, relevant content of some of at least one current hot topic is recommended to the user, or no relevant content of any current hot topic is recommended to the user.

[0053] In step 240 , the recommended content may be, for example, at least one of text content, audio content, image content, and video content.

[0054] In the example, since hot topics may be updated quickly, steps 210 to 240 may be performed based on a certain period (eg, 10 minutes) to further improve the accuracy of the determined current hot topics and the real-time nature of content push.

[0055] According to some embodiments, the target data includes at least one hot topic list, and step 230 includes:

[0056] Step 231: Use the large model to perform a first semantic similarity calculation on multiple first-listed topics included in at least one hot topic list to determine at least one current hot topic.

[0057] The current hot topic list is closely related to the current hot topics. Using the hot topic list as target data and input data for the large model can improve the accuracy, effectiveness and completeness of determining the current hot topics.

[0058] In addition, there may be multiple topics on the hot topic list that are related to the same current hot topic but have different language descriptions. Therefore, by performing semantic similarity calculation on them, it is possible to further ensure that there are no repeated parts in at least one current hot topic, thereby improving the efficiency of subsequent content recommendation.

[0059] In step 231 , illustratively, the hot topic list may be a list associated with the current hot topic, such as a video platform's playback popularity list and a social media platform's hot search list.

[0060] Figure 3 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown.

[0061] According to some embodiments, Figure 3 As shown, the method 200 further includes:

[0062] Step 310: For each current hot topic in at least one current hot topic,

[0063] Step 311: In response to determining that the target data includes a hot topic list, determine the popularity level of the current hot topic based on the number and ranking of the topics on the list associated with the current hot topic; and

[0064] Step 312: In response to determining that the target data includes multiple hot topic lists, determine the popularity level of the hot topic based on the number and ranking of the topics on the list associated with the current hot topic and the number of hot topic lists associated with the current hot topic; and

[0065] Step 320: Determine recommended content based on the preference characteristics and the popularity level of each current hot topic.

[0066] Different current hot topics have different popularity levels. Therefore, by further determining whether to push its related content to users based on the popularity level of the current hot topic, content recommendations can be made more targeted, thereby improving recommendation effects and user experience.

[0067] In step 311, when the target data includes only one hot topic list, its popularity level can be determined based on the number and ranking of the topics associated with each hot topic. The number and ranking of the topics associated with each hot topic are positively correlated with its popularity level.

[0068] For example, a first weight of the number of listed topics associated with each hot topic and a second weight of the ranking of the listed topics associated with the hot topic may be determined to determine its popularity level in a weighted manner.

[0069] In step 312, when the target data includes multiple hot topic lists, the number of hot topic lists associated with the current hot topic can be further introduced as an influencing factor for determining its popularity level. The number of hot topic lists associated with the current hot topic is positively correlated with its popularity level.

[0070] For example, the first weight of the topics and number associated with each hot topic, the second weight of the ranking of the topics associated with the hot topic, and the third weight of the number of hot topic lists associated with the hot topic can be determined to determine its popularity level in a weighted manner.

[0071] In the example, three different popularity levels can be set, for example, S level indicates a current hot topic with relatively high popularity, A+ level indicates a current hot topic with relatively medium popularity, and A level indicates a current hot topic with relatively low popularity.

[0072] Exemplarily, for users who have a high tendency to obtain related content of hot topics within the first time period, all related content of the current hot topics can be recommended to them; exemplarily, for users who have an average tendency to obtain related content of hot topics within the first time period, S-level and A+-level related content of the current hot topics can be recommended to them; exemplarily, for users who have a low tendency to obtain related content of hot topics within the first time period, only S-level related content of the current hot topics can be recommended to them.

[0073] Figure 4 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown.

[0074] According to some embodiments, Figure 4 As shown, step 320 includes:

[0075] Step 410: Determine the user's preference level based on the preference characteristics; and

[0076] Step 420: Determine recommended content based on the popularity level of each current hot topic and the preference level.

[0077] This allows users and current hot topics to be graded separately, and based on the degree of matching between the two, it determines whether to recommend content related to the current hot topic to the user. This effectively reduces processing difficulty and improves recommendation efficiency. Furthermore, by performing detailed grading of users and current hot topics, content recommendations can be made more targeted to users, further improving recommendation effectiveness and user experience.

[0078] In step 410, illustratively, users may be divided into levels 0-4 based on the degree of their tendency, where a user at a higher level has a higher tendency; illustratively, users may be scored based on their tendency to further refine the user classification.

[0079] In the example, for users with levels 3 and 4, content related to S, A+ and A levels and current hot topics can be recommended to them; for users with level 2, content related to S and A+ levels of current hot topics can be recommended to them; for users with level 1, content related to S levels of current hot topics can be recommended to them; and for users with level 0, no content related to current hot topics can be recommended to them.

[0080] It is understandable that the above-mentioned level settings of user preference, the above-mentioned level settings of popularity levels, and the level matching relationship when determining recommended content are only used for illustration purposes and are not limited thereto.

[0081] According to some embodiments, after step 240, method 200 further includes:

[0082] Step 250: For each current hot topic in at least one current hot topic, in response to determining the associated content of the current hot topic to be recommended to the user, a recall channel for the current hot topic is set in the recall model, wherein the recall model is used to obtain recommended content for recommendation to the user.

[0083] The content recommendation model includes a recall model and a ranking model. By setting corresponding recall channels for current hot topics that need to be recommended to users in the recall model, relevant content of the current hot topics can be effectively recalled to achieve content recommendation.

[0084] Figure 5 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown.

[0085] According to some embodiments, Figure 5 As shown, step 210 includes:

[0086] Step 510: Obtain at least one historical browsing content of the user;

[0087] Step 520: for each historical browsing content in at least one historical browsing content,

[0088] Step 521: Determine the content topic and browsing time of the historical browsing content;

[0089] Step 522: Acquire multiple historical hot topics associated with the browsing time point; and

[0090] Step 523: In response to determining that the content topic matches at least one of the multiple historical hot topics, determine that the historical browsing content is historical hot content; and

[0091] Step 530: Determine a preference feature based on the number of historical hotspot contents included in at least one historical browsing content.

[0092] In this way, based on the user's historical browsing content, the user's preference characteristics can be accurately determined to better determine whether to recommend relevant content of current hot topics to the user and which relevant content of current hot topics to recommend to the user, further improving user experience and content recommendation effects.

[0093] In step 510 , the historical browsing content may be, for example, at least one of text content, audio content, image content, and video content.

[0094] For example, the historical browsing content used to determine the user's preference characteristics can be in the same or different form as the ultimately determined pushed content. For example, the user's preference characteristics can be determined based on the text content they have historically browsed, and then the video content to be pushed to the user can be determined based on the preference characteristics and at least one current hot topic.

[0095] In step 521 , the browsing time point may be, for example, the date when the user browses the corresponding historical browsing content, or the moment when the user starts browsing the corresponding historical browsing content.

[0096] In step 523 , the historical browsing content may include one or more content topics. In response to at least one content topic matching at least one historical hot topic among the multiple historical hot topics, the historical browsing content is determined to be historical hot content.

[0097] In step 530 , the number of historical hot content is positively correlated with the user's preference.

[0098] For example, when the user has a small amount of historical browsing content, it is possible to further consider using the preference characteristics of users similar to the current user as a reference in determining the preference characteristics of the current user to improve the accuracy of the determined preference characteristics of the current user.

[0099] Figure 6 A partial flow chart of another content recommendation method according to an embodiment of the present disclosure is shown.

[0100] According to some embodiments, Figure 6 As shown, after step 530, the method 200 further includes:

[0101] Step 610: Obtain user feedback on each of at least one historical hot content to obtain at least one user historical feedback; and

[0102] Step 620: Determine the preference level based on at least one user's historical feedback.

[0103] By further introducing user feedback on historical hot content, the accuracy of the determined user preference features can be improved, so as to more accurately determine the recommended content for the user.

[0104] In step 610 , the feedback may be, for example, a like, a click of a “dislike” button, and the actual browsing time of the user.

[0105] According to some embodiments, the preference feature further indicates the type of content preferred by the user.

[0106] Therefore, by further introducing the user's preference for content type, it is possible to more accurately determine the recommended content for the user, effectively improving the user experience and recommendation effect.

[0107] For example, content types may include news content, film and television variety show content, and other content types. For example, in response to determining that the user prefers film and television variety show content, current hot topics associated with the film and television variety show content may be filtered out from at least one current hot topic to push related content, and so on.

[0108] For example, corresponding recall channels may be further set in the recall model for the content types preferred by the user to enable the recall of relevant content.

[0109] Figure 7 The figure shows a structural block diagram of a content recommendation device according to an embodiment of the present disclosure.

[0110] According to another aspect of the present disclosure, Figure 7As shown, a content recommendation device 700 is provided, including: a first module 710, configured to obtain a user's preference characteristics, wherein the preference characteristics indicate the user's tendency to obtain related content of a hot topic within a first time period, and the occurrence time of an associated event corresponding to the related content falls within the first time period; a second module 720, configured to obtain target data associated with the hot topic at the current moment; a third module 730, configured to process the target data using a large model to determine at least one current hot topic; and a fourth module 740, configured to determine recommended content for the user based on the preference characteristics and at least one current hot topic.

[0111] According to another aspect of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the aforementioned method.

[0112] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable the computer to execute the aforementioned method.

[0113] According to another aspect of the present disclosure, a computer program product is further provided, including a computer program, wherein the computer program implements the aforementioned method when executed by a processor.

[0114] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0115] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 can be any type of device that can input information to the electronic device 800. The input unit 806 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone and / or a remote control. The output unit 807 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator and / or a printer. The storage unit 808 can include but is not limited to a magnetic disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as Bluetooth TM devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0116] The computing unit 801 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the GPU-based matrix calculation method. For example, in some embodiments, the GPU-based matrix calculation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the GPU-based matrix calculation method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the GPU-based matrix calculation method by any other appropriate means (e.g., by means of firmware).

[0117] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0122] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0123] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0124] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A content recommendation method, comprising: Obtaining a user preference feature, wherein the preference feature indicates the user's tendency to obtain content associated with a hot topic within a first time period, and the occurrence time of an associated event corresponding to the associated content falls within the first time period; Obtain target data related to hot topics at the current moment; Processing the target data using a large model to determine at least one current hot topic; and Determine recommended content for the user based on the preference characteristics and the at least one current hot topic.

2. The method according to claim 1, wherein The target data includes at least one hot topic list, and the processing of the target data using the large model includes: The large model is used to calculate semantic similarity of multiple topics included in the at least one hot topic list to determine the at least one current hot topic.

3. The method according to claim 2, further comprising: For each current hot topic in the at least one current hot topic, In response to determining that the target data includes a hot topic list, determining a popularity level of the current hot topic based on the number and ranking of the topics on the list associated with the current hot topic; as well as In response to determining that the target data includes a plurality of hot topic lists, determining a popularity level of the hot topic according to the number and ranking of the listed topics associated with the current hot topic and the number of hot topic lists associated with the current hot topic; as well as The recommended content is determined based on the preference characteristics and the popularity level of each current hot topic.

4. The method according to claim 3, wherein: Determining the recommended content for the user based on the preference characteristics and the popularity level of each current hot topic includes: Determining the user's preference level based on the preference characteristics; and The recommended content is determined based on the popularity level of each current hot topic and the preference level.

5. The method according to any one of claims 1 to 4, further comprising: For each of the at least one current hot topic, in response to determining associated content of the current hot topic to be recommended to the user, a recall channel for the current hot topic is set in the recall model, wherein the recall model is used to obtain the recommended content to recommend to the user.

6. The method according to any one of claims 1 to 5, wherein The obtaining of user preference characteristics includes: Obtain at least one historical browsing content of the user; For each historical browsing content in the at least one historical browsing content, Determine the content topic and browsing time of the historical browsing content; Obtaining multiple historical hot topics associated with the browsing time point; and In response to determining that the content topic matches at least one of the plurality of historical hot topics, determining the historical browsing content as historical hot content; and The preference feature is determined according to the number of historical hot spots included in the at least one historical browsing content.

7. The method according to claim 6, wherein: In response to determining that the at least one historical browsing content includes at least one historical hotspot content, the method further includes: Obtaining feedback from the user on each of the at least one historical hot content to obtain at least one user historical feedback; and The preference level is determined according to the at least one user historical feedback.

8. The method according to any one of claims 1 to 7, wherein The preference feature further indicates the content type preferred by the user.

9. An information prompting device, comprising: A first module is configured to obtain a user preference feature, wherein the preference feature indicates the user's tendency to obtain related content of a hot topic within a first time period, and the occurrence time of the related event corresponding to the related content falls within the first time period; The second module is configured to obtain target data related to the hot topic at the current moment; A third module is configured to process the target data using a large model to determine at least one current hot topic; and The fourth module is configured to determine recommended content for the user based on the preference characteristics and the at least one current hot topic.

10. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

12. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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