Recommendation method and device, equipment and medium

Abstract

The invention provides a recommendation method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent recommendation and software application. According to the implementation scheme, in response to a received recommendation request for displaying recommendation information to a target user, first recommendation information is determined based on first data corresponding to the target user; displaying a first page including the first recommendation information to the target user; second recommendation information is determined based on second data corresponding to the target user, and the data size of the second data is larger than that of the first data; and in response to determining that the second recommendation information is different from the first recommendation information, updating the first recommendation information into the second recommendation information in the first page.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to the fields of intelligent recommendation and software application technology, and specifically relates to a recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Artificial intelligence is a discipline that studies how to make a computer simulate certain thinking processes and intelligent behaviors of humans (such as learning, reasoning, thinking, planning, etc.), and it has both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include several major directions such as computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, and knowledge graph technology.

[0003] With the development of computer technology and big data technology, behaviors such as consumption, entertainment, learning, and travel in people's lives are all closely related to big data. During the operation of a software platform, it is usually necessary to actively recommend content to users.

[0004] The methods described in this section are not necessarily methods that have been previously envisioned or adopted. Unless otherwise specified, any method described in this section should not be considered prior art solely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0005] The present disclosure provides a recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0006] According to an aspect of the present disclosure, there is provided a recommendation method, including: in response to receiving a recommendation request to display recommendation information to a target user, determining first recommendation information based on first data corresponding to the target user; displaying a first page including the first recommendation information to the target user; determining second recommendation information based on second data corresponding to the target user, where the data volume of the second data is greater than the data volume of the first data; and in response to determining that the second recommendation information is different from the first recommendation information, updating the first recommendation information to the second recommendation information in the first page.

[0007] According to another aspect of the present disclosure, there is provided a recommendation device, including: a first determination unit configured to determine first recommendation information based on first data corresponding to a target user in response to receiving a recommendation request for displaying recommendation information to the target user; a display unit configured to display a first page including the first recommendation information to the target user; a second determination unit configured to determine second recommendation information based on second data corresponding to the target user, wherein the amount of data of the second data is greater than that of the first data; and an update unit configured to update the first recommendation information to the second recommendation information in the first page in response to determining that the second recommendation information is different from the first recommendation information.

[0008] According to another aspect of the present disclosure, there is provided an electronic device, including: 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 above-mentioned recommendation method.

[0009] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the above-mentioned recommendation method.

[0010] According to another aspect of the present disclosure, there is provided a computer program product including a computer program, wherein the computer program can implement the above-mentioned recommendation method when executed by a processor.

[0011] According to one or more embodiments of the present disclosure, the display efficiency of recommendation information can be improved.

[0012] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used 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

[0013] The drawings exemplarily illustrate embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0014] Figure 1 A schematic diagram of an exemplary system in which various methods described herein can be implemented according to an exemplary embodiment of the present disclosure is shown;

[0015] Figure 2Shows a flowchart of a recommendation method according to an exemplary embodiment of the present disclosure;

[0016] Figure 3 Shows a schematic diagram of the update process of recommendation information according to an exemplary embodiment of the present disclosure;

[0017] Figure 4 Shows a structural block diagram of a recommendation device according to an exemplary embodiment of the present disclosure;

[0018] Figure 5 Shows a structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure. Detailed implementation manners

[0019] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0020] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, timing 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, and in certain cases, based on the context description, they may also refer to different instances.

[0021] In the descriptions of various examples in the present disclosure, the terms used are only for the purpose of describing specific examples 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 the present disclosure covers any one of the listed items and all possible combinations.

[0022] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0023] Figure 1 Shows a schematic diagram of an exemplary system 100 in which the various methods and devices described herein can be implemented according to an embodiment of the present disclosure. Refer to 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 that couple the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0024] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable a recommendation method to be executed.

[0025] In certain embodiments, the server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of the client devices 101, 102, 103, 104, 105, and / or 106 under a software-as-a-service (SaaS) model.

[0026] In Figure 1 In the depicted configuration, 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 may be executed by one or more processors. Users operating the 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 various different system configurations are possible, which may be different from the system 100. Therefore, Figure 1 is an example of a system for implementing the various methods described herein and is not intended to be limiting.

[0027] Users may use the client devices 101, 102, 103, 104, 105, and / or 106 to send recommendation requests or perform interaction operations on recommended content. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although Figure 1 only six client devices are depicted, those skilled in the art will be able to understand that the present disclosure may support any number of client devices.

[0028] Client devices 101, 102, 103, 104, 105, and / or 106 may include various categories of computing devices such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), 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 computing devices may run various categories 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 WindowsMobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smartphones, tablets, 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 various different applications such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0029] Network 110 may be any category of network known to those skilled in the art, which may support data communication using any one of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 110 may be a local area network (LAN), an Ethernet-based network, token ring, wide area network (WAN), the Internet, virtual network, virtual private network (VPN), intranet, extranet, blockchain network, public switched telephone network (PSTN), infrared network, wireless network (such as Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0030] Server 120 may include one or more general-purpose computers, dedicated server computers (such as PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframes, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices for the server). In various embodiments, server 120 may run one or more services or software applications that provide the functions described below.

[0031] The computing units in server 120 can run one or more operating systems including any of the above operating systems and any commercially available server operating systems. Server 120 can also run any one of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0032] In some embodiments, server 120 can include one or more applications to analyze and combine data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 can also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0033] In some embodiments, server 120 can be a server of a distributed system or a server incorporating a blockchain. Server 120 can also be a cloud server or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system to address the defects of high management difficulty and weak business scalability existing in traditional physical hosts and virtual private server (VPS) services.

[0034] System 100 can also include one or more databases 130. In certain embodiments, these databases can be used to store data and other information. For example, one or more of databases 130 can be used to store information such as audio files and video files. Databases 130 can reside in various locations. For example, the databases used by server 120 can be local to server 120 or can be remote from server 120 and can communicate with server 120 via a network-based or dedicated connection. Databases 130 can be of different categories. In certain embodiments, the databases used by server 120 can be relational databases, for example. One or more of these databases can store, update, and retrieve data to and from the databases in response to commands.

[0035] In certain embodiments, one or more of databases 130 can also be used by applications to store application data. The databases used by applications can be different categories of databases, such as key-value repositories, object repositories, or conventional repositories supported by a file system.

[0036] Figure 1System 100 can be configured and operated in various ways to enable the application of various methods and devices described in this disclosure.

[0037] In the related art, when it is necessary to display recommended information to a user on a front-end page, it is usually to determine the recommended information first after receiving a recommended information display request and then render the display page. However, since the recommended information is usually generated based on complex recommendation strategies (for example, by calling a recommendation model to determine the recommended information), the response time of the recommendation strategy may be relatively long, resulting in a relatively long page rendering duration and low display efficiency.

[0038] Based on this, this disclosure provides a recommendation method. When a recommendation request is received, first determine first recommended information based on a small amount of information, render and display an initial first page based on this to improve the display efficiency of the first page, and then further determine second recommended information based on more information and dynamically update the content displayed on the first page, so as to be able to ensure recommendation accuracy while improving the page display efficiency.

[0039] Figure 2 The flowchart of a recommendation method 200 according to an exemplary embodiment of this disclosure is shown. As Figure 2 shown, method 200 includes:

[0040] Step S201, in response to receiving a recommendation request to display recommended information to a target user, determine first recommended information based on first data corresponding to the target user;

[0041] Step S202, display a first page including the first recommended information to the target user;

[0042] Step S203, determine second recommended information based on second data corresponding to the target user, where the amount of data of the second data is greater than the amount of data of the first data; and

[0043] Step S204, in response to determining that the second recommended information is different from the first recommended information, update the first recommended information to the second recommended information on the first page.

[0044] By applying the above method 200, it is possible to first determine first recommended information based on a small amount of information (first data) when a recommendation request is received, render and display an initial first page based on this to improve the display efficiency of the first page. Since the amount of information of the first data used to determine the first recommended information is small, steps S203 and 204 are further applied in method 200 to further determine more accurate second recommended information based on more information (second data) and dynamically update the content displayed on the first page, so as to be able to ensure recommendation accuracy while improving the page display efficiency.

[0045] In some examples, the second data may include the first data to introduce more user feature information based on the first data, and based on this, more accurate second recommendation information is determined. In some examples, the second data may also not include the first data. As long as in step S203, more accurate second recommendation information can be re-determined based on the second data that is richer and more comprehensive than the first data, the present disclosure does not limit the specific content of the second data.

[0046] According to some embodiments, the recommendation request is triggered based on the search request of the target user, and the first data includes the user search text included in the search request. Thus, relevant content can be recommended to the user when the user conducts a search, so as to improve the user experience.

[0047] In some examples, in step S201, the first recommendation information to be recommended to the user may be determined based on the user search text input by the user. For example, the first recommendation information may be selected from multiple candidate information based on the relevance between the multiple candidate information and the user search text, so as to improve the determination efficiency of the first recommendation information, and then quickly render the page containing the first recommendation information to improve the page display efficiency.

[0048] In some examples, the recommendation information may be a recommended search text, so that the user can trigger a search process based on the recommended search text by performing an interaction operation on the first page, and then view the search result page corresponding to the recommended search text, meeting the user's information viewing needs and improving the recommendation effect.

[0049] According to some embodiments, the second data includes at least one of the following: at least one historical search text searched by the target user and the search behavior information corresponding to the at least one historical search text; at least one historical content browsed by the target user and the browsing behavior information corresponding to the at least one historical content; and the trigger behavior information for the target user to send the search request. Thus, the second recommendation information can be determined based on rich user behavior information to improve the recommendation accuracy.

[0050] In some examples, the search behavior information and browsing behavior information of the target user can be stored offline in a database. Thus, the historical interaction data of the target user can be directly obtained by querying the database, and the user interest can be indicated based on this. In one example, when the user triggers a search process based on the user's search text, the search behavior information and browsing behavior information of the target user can be obtained by querying the offline database. Then, the weight corresponding to each historical search text can be determined based on the search behavior information (such as the number of searches, search duration, etc.) corresponding to each historical search text, and the weight corresponding to each historical content can be determined based on the browsing behavior information (such as browsing duration, number of clicks, etc.) corresponding to each historical content. On this basis, the second recommendation information can be determined from multiple historical search texts or multiple historical contents based on the weights of each historical search text and the weights of each historical content.

[0051] In some examples, the second recommendation information is determined from a candidate content library. In this case, the second recommendation information can also be determined based on the weights of each historical search text, the weights of each historical content, and the relevance between the historical search texts, historical contents, and candidate content. As long as the user interest can be indicated based on the user's historical interaction data and the second recommendation information that meets the user interest can be determined based on this, the specific determination method of the second recommendation information in the present disclosure is not limited.

[0052] In some examples, the triggering actions of the target user can include: actively entering a search text in the search box to trigger the search result display page, clicking on a search entry in the hot search content list to quickly trigger the search result display page, and clicking on a search entry in the recommended card to quickly trigger the search result display page. It should be understood that the triggering method corresponding to the display page can indicate the user interest to a certain extent. For example, when the user triggers the search by clicking on the hot search content list, it indicates that the user may be more sensitive to the timeliness or popularity of the search content. Then, the recommendation information that can better meet the user's needs can be determined based on this to improve the recommendation effect.

[0053] According to some embodiments, determining the second recommendation information based on the second data corresponding to the target user in step S203 includes: inputting the first data into a recommendation model to obtain the second recommendation information output by the recommendation model. Thus, the recommendation model can be used to obtain more accurate second recommendation information based on richer input parameters (second data), improving the recommendation accuracy.

[0054] In some examples, step S201 can also be implemented using a recommendation model, that is, inputting the first data into the recommendation model to obtain the first recommendation information output by the recommendation model. In this example, the recommendation model can include multiple input modules. For example, when method 200 is applied to search for products and the target user can trigger a recommendation request using a search request, the input modules of the recommendation model can include a user search text module, a user historical search behavior information module, a user historical browsing behavior information module, and a trigger behavior information module. In this case, in step S201, only the user search text can be passed into the user search text module, and the input information of the user historical search behavior information module, the user historical browsing behavior information module, and the trigger behavior information module can be set to 0, so that the recommendation model can perform rapid inference based on the user search text to efficiently obtain the first recommendation information. On this basis, in step S203, the user search text, the user historical search behavior information, the user historical browsing behavior information, and the trigger behavior information can all be input into the recommendation model, so that the recommendation model can output a second recommendation information that better meets the needs of the target user, and based on this, update the content displayed on the front-end page to ensure recommendation accuracy. In some examples, the recommendation model can be trained using sample data including sample input information, thereby improving the recommendation efficiency and accuracy.

[0055] According to some embodiments, determining the second recommendation information based on the second data corresponding to the target user in step S203 includes: in response to determining that the first page has been rendered, determining the second recommendation information based on the second data corresponding to the target user. Method 200 further includes: in response to determining that the interaction duration of the target user with the first page exceeds a duration threshold, determining a third recommendation information different from the current recommendation information displayed on the first page; and updating the current recommendation information on the first page to the third recommendation information.

[0056] Thus, it is possible to start the step of determining the second recommendation information immediately after the first page is rendered and presented, so as to achieve the first update of the recommended content displayed on the front-end page. On this basis, the recommended information displayed on the front-end can be updated again when the interaction duration of the user with the first page exceeds the threshold, so as to further improve the recommendation accuracy.

[0057] As mentioned above, the recommended content displayed on the front-end page will only be updated when the second recommendation information output in step S203 is different from the initial first recommendation information. Therefore, the current recommendation information displayed on the first page may be the first recommendation information initially displayed on the first page, or may be the updated second recommendation information.

[0058] According to some embodiments, determining the third recommendation information different from the current recommendation information of the first page display includes: determining the third recommendation information based on the content interaction behavior information of the target user in the first page. Thereby, it is possible to determine recommendation information that better meets the user's needs based on the user's real-time interaction information on the front-end page, so as to improve the recommendation accuracy.

[0059] In some examples, the interaction operations of the user on the first page indicated by the content interaction behavior information may include one or more of various interaction operations such as clicking, selecting content, sharing content, copying content, etc. For example, it may be an operation such as clicking on a link in the first page, selecting text or a picture in the first page, copying text or a picture in the first page, sharing the first page or the content in the first page to other pages or sessions or software platforms. Based on the user's content interaction behavior information, the degree of interest of the user in the page content can be more accurately indicated, and more accurate recommendations can be realized based on this.

[0060] In some examples, the step of determining the third recommendation information based on the content interaction behavior information of the target user in the first page can also be implemented using the above-mentioned recommendation model. In one example, when the input information of the recommendation model includes the user's real-time content interaction behavior information, the weight of the real-time content interaction behavior information can also be adjusted simultaneously, so that the model can determine the third recommendation information that better meets the user's real-time needs based on the content interaction behavior information, so as to improve the recommendation accuracy.

[0061] In some examples, the step of determining the third recommendation information based on the target user's real-time content interaction behavior information can also be implemented in other ways. For example, it may be based on one or more contents interacted by the user in the first page and the interaction data corresponding to each content, determine at least one recalled content from the one or more contents interacted by the user, and then determine the third recommendation information from the at least one recalled content or determine the third recommendation information related to the at least one recalled content. Another example is that it may also be based on the interaction data corresponding to each content to determine the corresponding weight of each content, and based on this determine the third recommendation information to improve the recommendation accuracy.

[0062] According to some embodiments, the recommendation request is triggered based on the search request of the target user, the first page includes the search result entries corresponding to the search request, and the interaction duration of the target user with the first page includes the sum of the following items: the first browsing duration of the target user browsing the first page; and the second browsing duration of the target user browsing the jump page of the search result entries in the first page.

[0063] Therefore, when the recommended information is determined based on the user's search request and displayed on the search result page, it is possible to determine whether it is necessary to update the recommended information on the search result page based on the browsing duration of the user on the search result page and the search result jump page. It should be understood that the total browsing duration of the user on the search result page and the search result jump page corresponds to the total duration of the user browsing the search results of the corresponding user search text on the first page. In this case, by monitoring the browsing duration of the user for the user search text, it is possible to more accurately determine whether it is necessary to recommend a new recommended search text to the user, so as to increase the probability of the user triggering the recommended search text and improve the recommendation effect.

[0064] According to some embodiments, determining the third recommended information different from the current recommended information displayed on the first page in response to determining that the interaction duration of the target user with the first page exceeds the duration threshold includes: creating a first timer for monitoring the first browsing duration, where the first timer is used to send a request to obtain the third recommended information when the timing value exceeds the duration threshold; in response to determining that the target user enters the jump page of the search result entry in the first page, creating a second timer for monitoring the second browsing duration, where the second timer is used to send a request to obtain the third recommended information when the timing value exceeds the difference between the duration threshold and the first browsing duration; and in response to determining that the target user returns from the jump page to the first page, updating the timing value of the first timer based on the timing value of the second timer. Thus, it is possible to use the page timer to achieve segmented timing and statistics of the search result page and the landing page, so as to more simply and accurately determine the interaction duration of the user.

[0065] Figure 3 FIG. shows a schematic diagram of the update process of the recommended information according to an exemplary embodiment of the present disclosure. As Figure 3 shown, the update process of the recommended information includes the following steps:

[0066] Based on the search result page timer (first timer) of the first page, the following steps S11 - step S13 can be achieved:

[0067] Step S11, obtain the remaining target duration. In this example, the remaining target duration can be determined based on the duration threshold and the current first browsing duration corresponding to the first page.

[0068] Step S12, update the remaining target duration. When the user finishes browsing in the jump page, the monitoring information of the search result page timer can be updated based on the second browsing duration corresponding to the jump page. That is, subtract the second browsing duration from the remaining target duration obtained in step S11 to determine the remaining duration for updating the recommended information on the front-end page.

[0069] Step S13, Dynamically update the front-end display content. When the remaining target duration is 0, the step of dynamically updating the recommended content displayed on the front-end page is triggered.

[0070] The following steps S21 - S23 can be implemented in the jump page:

[0071] Step S21, Create a jump page timer (corresponding to the second timer described above). The trigger threshold of the jump page timer is determined based on the remaining target duration obtained in step S11.

[0072] Step S22, In response to leaving the jump page, remove the jump page timer. When the user finishes browsing in the jump page, the second browsing duration monitored by the jump page timer needs to be passed to the search result page timer to update the remaining target duration monitored by the search result page.

[0073] Step S23, The jump page timer is triggered. When the jump page timer is triggered, it is equivalent to the remaining target duration being 0, and then the step of dynamically updating the recommended content displayed on the front-end page is triggered.

[0074] By using the page timer to separately execute the segmented timing steps for the search result page and the landing page and perform statistics, the interaction duration of the user with the first page can be determined more simply and accurately, so as to determine the timing of dynamically updating the recommended content displayed on the front-end page, thereby improving the recommendation accuracy.

[0075] In some examples, the operations of obtaining the remaining target duration, updating the remaining target duration, monitoring the duration trigger information, and dynamically updating the front-end display content can be implemented by defining action functions in the time management class. As long as the monitoring of the browsing duration of one or more pages can be achieved and based on this, it is determined whether the recommended content displayed on the front-end page needs to be updated, the present disclosure does not limit the specific duration monitoring and timing trigger methods.

[0076] According to some embodiments, updating the current recommended information to the third recommended information in the first page includes: in response to determining that the target user does not interact with the current recommended information, updating the current recommended information to the third recommended information. Thus, it is possible to avoid updating the content displayed on the front-end page when the user has already interacted with the recommended information, thereby avoiding affecting the user experience.

[0077] Figure 4 The block diagram of a recommendation device 400 according to an exemplary embodiment of the present disclosure is shown. As Figure 4 shown, the device 400 includes:

[0078] A first determination unit 401, configured to determine first recommendation information based on first data corresponding to the target user in response to receiving a recommendation request to display recommendation information to the target user;

[0079] A display unit 402, configured to display a first page including the first recommendation information to the target user;

[0080] A second determination unit 403, configured to determine second recommendation information based on second data corresponding to the target user, wherein the amount of data of the second data is greater than the amount of data of the first data; and

[0081] An update unit 404, configured to update the first recommendation information to the second recommendation information in the first page in response to determining that the second recommendation information is different from the first recommendation information.

[0082] According to some embodiments, the second determination unit 403 is configured to: in response to determining that the first page is rendered, determine the second recommendation information based on second data corresponding to the target user. The apparatus 400 further includes: a third determination unit, configured to determine third recommendation information different from the current recommendation information displayed on the first page in response to determining that the interaction duration of the target user with the first page exceeds a duration threshold. The update unit 404 is further configured to update the current recommendation information to the third recommendation information in the first page.

[0083] According to some embodiments, the third determination unit is configured to: determine the third recommendation information based on content interaction behavior information of the target user in the first page.

[0084] According to some embodiments, the recommendation request is triggered based on a search request of the target user. The first page includes search result entries corresponding to the search request. The interaction duration of the target user with the first page includes the sum of the following: a first browsing duration for the target user to browse the first page; and a second browsing duration for the target user to browse a jump page of the search result entries in the first page.

[0085] According to some embodiments, the third determination unit includes: a first creation subunit configured to create a first timer for monitoring the first browsing duration, where the first timer is configured to send a request for obtaining the third recommendation information when a timing value exceeds the duration threshold; a second creation subunit configured to, in response to determining that the target user enters a jump page of a search result entry in the first page, create a second timer for monitoring the second browsing duration, where the second timer is configured to send a request for obtaining the third recommendation information when a timing value exceeds a difference between the duration threshold and the first browsing duration; and an update subunit configured to, in response to determining that the target user returns from the jump page to the first page, update the timing value of the first timer based on the timing value of the second timer.

[0086] According to some embodiments, the update unit 404 is configured to: in response to determining that the target user does not interact with the current recommendation information, update the current recommendation information to the third recommendation information.

[0087] According to some embodiments, the recommendation request is triggered based on a search request of the target user, and the first data includes a user search text included in the search request.

[0088] According to some embodiments, the second data includes at least one of the following: at least one historical search text historically searched by the target user and search behavior information corresponding to the at least one historical search text; at least one historical content historically browsed by the target user and browsing behavior information corresponding to the at least one historical content; and trigger behavior information for the target user to send the search request.

[0089] According to some embodiments, the second determination unit 403 is configured to input the first data into a recommendation model to obtain the second recommendation information output by the recommendation model.

[0090] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0091] According to another aspect of the present disclosure, there is also provided an electronic device, including: 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 when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the above-mentioned recommendation method.

[0092] According to another aspect of the present disclosure, there is also provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the above-mentioned recommendation method.

[0093] According to another aspect of the present disclosure, there is also provided a computer program product, including a computer program, wherein the computer program implements the above-mentioned recommendation method when executed by a processor.

[0094] Referring to Figure 5 , a block diagram of an electronic device 500 that can be a server or a client of the present disclosure will now be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0095] As Figure 5 shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 502 or the computer program loaded from the storage unit 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0096] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. The input unit 506 can be any type of device capable of inputting information to device 500. The input unit 506 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function control of the electronic device, and can include but not be limited to a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone, and / or remote control. The output unit 507 can be any type of device capable of presenting information, and can include but not be limited to a display, speaker, video / audio output terminal, vibrator, and / or printer. The storage unit 508 can include but not be limited to magnetic disks, optical disks. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include but not be limited to a modem, network card, infrared communication device, wireless communication transceiver, and / or chipset, such as a BluetoothTM device, 802.11 device, WiFi device, WiMax device, cellular communication device, and / or the like.

[0097] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 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 running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as the recommendation method. For example, in some embodiments, the recommendation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the recommendation method described above can be executed. Alternatively, in other embodiments, the computing unit 501 can be configured to execute the recommendation method in any other suitable manner (e.g., by means of firmware).

[0098] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (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 interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0099] The program code for implementing the methods 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, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0100] 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 connection with an instruction execution system, apparatus, or device. 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball), by which the user can provide input to the computer. Other kinds 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 the input received from the user can be in any form (including acoustic input, speech input, or tactile input).

[0102] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), the Internet, and blockchain networks.

[0103] A computer system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating blockchain.

[0104] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed 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, and no limitations are imposed herein.

[0105] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above 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 defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples may be omitted or replaced by their equivalent elements. In addition, the steps may be executed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein may be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A recommendation method, comprising: In response to receiving a recommendation request for displaying recommendation information to a target user, determining first recommendation information based on first data corresponding to the target user; Displaying a first page including the first recommendation information to the target user; Determining second recommendation information based on second data corresponding to the target user, wherein the amount of data of the second data is greater than the amount of data of the first data; And In response to determining that the second recommendation information is different from the first recommendation information, updating the first recommendation information to the second recommendation information in the first page.

2. The method according to claim 1, wherein, The determining second recommendation information based on second data corresponding to the target user includes: In response to determining that the first page is rendered completely, determining the second recommendation information based on second data corresponding to the target user, The method further includes: In response to determining that the interaction duration of the target user with the first page exceeds a duration threshold, determining third recommendation information different from the current recommendation information displayed on the first page; and Updating the current recommendation information to the third recommendation information in the first page.

3. The method according to claim 2, wherein The determining third recommendation information different from the current recommendation information displayed on the first page includes: Determining the third recommendation information based on content interaction behavior information of the target user in the first page.

4. The method according to claim 2 or 3, wherein The recommendation request is triggered based on a search request of the target user, the first page includes search result entries corresponding to the search request, and the interaction duration of the target user with the first page includes the sum of the following: A first browsing duration for the target user to browse the first page; And A second browsing duration for the target user to browse a jump page of a search result entry in the first page.

5. The method according to claim 4, wherein, The in response to determining that the interaction duration of the target user with the first page exceeds a duration threshold, determining third recommendation information different from the current recommendation information displayed on the first page includes: Creating a first timer for monitoring the first browsing duration, the first timer being configured to send a request for obtaining the third recommendation information when a timing value exceeds the duration threshold; In response to determining that the target user enters a jump page of a search result entry in the first page, creating a second timer for monitoring the second browsing duration, the second timer being configured to send a request for obtaining the third recommendation information when a timing value exceeds the difference between the duration threshold and the first browsing duration; and In response to determining that the target user returns from the jump page to the first page, updating the timing value of the first timer based on the timing value of the second timer.

6. The method according to any one of claims 2-5, wherein, The updating the current recommendation information to the third recommendation information in the first page includes: In response to determining that the target user does not interact with the current recommendation information, updating the current recommendation information to the third recommendation information.

7. The method according to any one of claims 1-6, wherein, The recommendation request is triggered based on a search request of the target user, and the first data includes the user search text included in the search request.

8. The method according to claim 7, wherein, The second data includes at least one of the following: At least one historical search text of the target user's historical searches and search behavior information corresponding to the at least one historical search text; At least one historical content of the target user's historical browsing and browsing behavior information corresponding to the at least one historical content; And Trigger behavior information for the target user to send the search request.

9. The method according to any one of claims 1-8, wherein, The determining the second recommendation information based on the second data corresponding to the target user includes: Inputting the first data into a recommendation model to obtain the second recommendation information output by the recommendation model.

10. A recommendation device, comprising: A first determining unit, configured to determine first recommendation information based on first data corresponding to a target user in response to receiving a recommendation request to display recommendation information to the target user; A display unit, configured to display a first page including the first recommendation information to the target user; A second determining unit, configured to determine second recommendation information based on second data corresponding to the target user, wherein the data volume of the second data is greater than the data volume of the first data; And An update unit, configured to update the first recommendation information to the second recommendation information in the first page in response to determining that the second recommendation information is different from the first recommendation information.

11. An electronic device, 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 method according to any one of claims 1-9.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause a computer to execute the method according to any one of claims 1-9.

13. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method according to any one of claims 1-9.

Images