CN117992675A - Content recommendation method and device, electronic equipment and storage medium - Google Patents

Abstract

The disclosure provides a content recommendation method, a content recommendation device, electronic equipment and a storage medium, relates to the technical field of Internet and the technical field of artificial intelligence, and particularly relates to the fields of Internet searching, large language models, machine learning, deep learning and the like. The implementation scheme is as follows: generating preset keywords for content recommendation; determining whether the search keywords are matched with preset keywords; determining whether to recommend content for the current search based on the identification information in response to determining that the search keyword matches a preset keyword; and responsive to determining that content recommendations are to be made for the current search, presenting associated information associated with the content recommendations.

Description

Content recommendation method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of internet technology and the field of artificial intelligence technology, and in particular, to the fields of internet search, large language model, machine learning, deep learning, etc., and more particularly, to a content recommendation method, apparatus, electronic device, computer readable storage medium, and computer program product.

Background

In recent years, with the rising and development of the field of artificial intelligence technology, the industry is gradually focusing on the deep combination of artificial intelligence technology with application fields such as electronic commerce, internet and the like, so as to obtain wider application.

Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.

Disclosure of Invention

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

According to an aspect of the present disclosure, there is provided a content recommendation method including: generating preset keywords for content recommendation; determining whether the search keywords are matched with preset keywords; determining whether to recommend content for the current search based on the identification information in response to determining that the search keyword matches a preset keyword; and responsive to determining that content recommendations are to be made for the current search, presenting associated information associated with the content recommendations.

According to another aspect of the present disclosure, there is provided a content recommendation apparatus including: the keyword generation module is configured to generate preset keywords for content recommendation; the matching determining module is configured to determine whether the search keyword is matched with a preset keyword; a content recommendation determination module configured to determine whether to recommend content for the current search based on the identification information in response to determining that the search keyword matches a preset keyword; and a presentation module configured to present associated information associated to the content recommendation in response to determining that the content recommendation is to be made for the current search.

According to another aspect of the present disclosure, there is provided an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described above.

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

According to one or more embodiments of the present disclosure, it may be convenient to promote the effect of content recommendation.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a flow chart of a content recommendation method according to an embodiment of the present disclosure;

FIG. 3 illustrates a flowchart of a process of generating preset keywords for making content recommendations, according to an embodiment of the present disclosure;

FIG. 4 illustrates a flowchart of a process of determining whether to make a content recommendation for a current search based on identification information, according to an embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of the structure of a conversion determination model according to an embodiment of the present disclosure;

FIG. 6 illustrates a flow chart of a content recommendation method according to another embodiment of the present disclosure;

FIG. 7 shows a block diagram of a content recommendation device according to one embodiment of the present disclosure;

FIG. 8 shows a block diagram of a content recommendation device according to another embodiment of the present disclosure; and

Fig. 9 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another element. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.

The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.

In the related art, the conventional content recommendation method generally increases or expands the recommendation range to enhance the effect of content recommendation responded by the user, and in this process, additional interference and influence may be caused to the user who does not desire to receive the content recommendation or does not have a demand for the content recommendation. In addition, in the conventional content recommendation method, related staff is required to perform keyword expansion work according to own experience, internet statistics tools, search word reports and the like. These factors may cause problems such as poor effect, low degree of intelligence, etc. of the conventional content recommendation method.

In view of at least one of the above problems, embodiments of the present disclosure provide a content recommendation method.

Before describing in detail the methods of embodiments of the present disclosure, an exemplary system in which the methods described herein may be implemented is first described in connection with fig. 1.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 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. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the content recommendation method.

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

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user operating client devices 101, 102, 103, 104, 105, and/or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may input search keywords using client devices 101, 102, 103, 104, 105, and/or 106. 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 fig. 1 depicts only six client devices, those skilled in the art will appreciate that the present disclosure may support any number of client devices.

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 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, and the like. 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 (e.g., GOOGLE Chrome OS); or include various mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablet computers, personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays (such as smart glasses) and other devices. The gaming system may include various handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. For 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 blockchain network, 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.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in 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. 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, etc.

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/or 106. Server 120 may 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/or 106.

In some implementations, the server 120 may be a server of a distributed system or a server that incorporates a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and Virtual special server (VPS PRIVATE SERVER) service.

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

In some embodiments, one or more of databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.

Various aspects of a content recommendation method according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 2 shows a flowchart of a content recommendation method according to an embodiment of the present disclosure.

As shown in fig. 2, the content recommendation method 200 according to the embodiment of the present disclosure includes steps S202, S204, S206, and S208.

In step S202, a preset keyword for making content recommendation is generated.

In step S204, it is determined whether the search keyword matches a preset keyword.

In step S206, in response to determining that the search keyword matches the preset keyword, it is determined whether or not to make a content recommendation for the current search based on the identification information.

In step S208, in response to determining that content recommendation is to be made for the current search, association information associated to the content recommendation is presented.

In some embodiments, the content recommendation method according to embodiments of the present disclosure may be applied, for example, in an application program providing a search or search function, so that when a search request is received, a scene suitable for triggering content recommendation may be created. In some examples, the content recommendation may include, for example, a recommendation of an existing function or a new function in the application.

In some embodiments, a preset keyword for triggering content recommendation is generated in advance, and the preset keyword is used for matching with a search keyword, so as to preliminarily judge whether a search request related to the search keyword has relevance or association with content recommendation to be performed. In some examples, the preset keywords may include, for example, search common words or hot words with a large search amount, and the like. When it is determined initially that there is a relevance or a relevance, it is further determined secondarily based on the identification information whether or not to make a content recommendation for the current search. In some examples, the identifying information may include, for example, information related to the user performing the current retrieval. Further, if it is judged that the content recommendation can be made, the association information associated to the content recommendation is presented in the application or applet. In some examples, the association information may include, for example, a brief description of the content recommendation.

Therefore, the content recommendation method according to the embodiment of the disclosure can improve the effectiveness of content recommendation through the two judging processes by means of keyword matching and identification information, so that a scene suitable for triggering content recommendation can be created when a search request is received, and the effect of content recommendation is improved conveniently.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

Hereinafter, various aspects of the content recommendation method according to the embodiments of the present disclosure will be described in further detail.

Fig. 3 illustrates a flowchart of a process of generating preset keywords for making content recommendations according to an embodiment of the present disclosure.

As shown in fig. 3, the preset keyword generation process 300 according to an embodiment of the present disclosure may include steps S302, S304, and S306.

In step S302, seed keywords may be generated based on the large language model.

In step S304, the seed keywords may be expanded to generate candidate keywords.

At step S306, at least a part of the candidate keywords satisfying the predetermined condition may be selected as the preset keywords.

According to the preset keyword generation process, the intelligent interaction capability and the language capability of the large language model can be used for assisting manpower to generate wider and diversified seed keywords, so that the possibility of matching with the search keywords is improved, whether the corresponding search request has relevance or association with the content recommendation to be performed can be judged more accurately, and the creation of a scene suitable for triggering the content recommendation is facilitated.

In some embodiments, seed keywords may be generated based on a large language model. In order to facilitate the enhancement of the content recommendation effect, it is expected that a large language model can generate, for example, a search term or a hot word with a large search amount as a seed keyword. The large language model may be interacted with by input hinting (Prompt) to get a corresponding reply.

In some embodiments, after the large language model generates seed keywords, it may not yet be possible to determine whether the seed keywords are search terms or large search terms. Thus, interactions with the large language model may continue to be performed to determine this. For example, to learn the criteria and logic of the large language model to generate seed keywords, the following campt may be provided to the large language model: "how you determine these keywords is to retrieve common words or hot words with a large retrieval amount". Based on the reply of the large language model, for example, "whether the keyword is the search term or the hot word with large search amount needs to comprehensively consider a plurality of factors including search trend, keyword tool, user investigation and the like", it can be seen how to judge whether the keyword is the search term or the hot word with large search amount. Next, it may be further queried whether the large language model can determine the hotness of the seed keywords. For example, the following prompt may be provided to the large language model: "how do you help me query the heat analysis tool when providing these keywords? What the heat of these words is. However, based on the reply of a large language model, such as "very sorry, i cannot query the popularity analysis tool to obtain the popularity of these keywords", it can be seen that it does not have the ability to truly determine the popularity of keywords. To this end, a tool having a heat analysis may be further utilized to determine seed keywords in which predetermined conditions are met.

In some embodiments, the seed keywords may also be expanded first to generate candidate keywords. In an example, seed keywords may be expanded with a tool having a word expansion function to generate candidate keywords. For example, several or tens of seed keywords may be expanded into tens or even hundreds of candidate keywords. Then, a hotness analysis tool can be used for determining which candidate keywords are common search words or hot words with large search quantity, and then the candidate keywords can be selected as preset keywords.

In some embodiments, the preset keywords may be assigned corresponding keyword identifications.

In an example, a keyword identification may be automatically generated for each preset keyword after the preset keywords are generated. Thereafter, keyword identification may be added to a landing page (i.e., a page on which content recommendation is performed) of a preset keyword, for example: com keyid = yy, enabling keyword tracking.

Thus, the keyword identification may be used to indicate based on which keyword the associated information of the content recommendation was clicked. Therefore, the keyword can be tracked to improve the use efficiency of the keyword, and further the possibility of matching with the search keyword is improved.

In some embodiments, the large language model may continue to be utilized to generate association information associated with content recommendations for presentation. Fig. 4 illustrates a flowchart of a process of determining whether to make a content recommendation for a current search based on identification information, according to an embodiment of the present disclosure.

As shown in fig. 4, process 400 may include steps S402, S404, and S406.

In step S402, a plurality of features of the user performing the current search may be determined based on the identification information.

In step S404, it may be determined whether there is at least one feature of the plurality of features that matches the preset target feature.

In step S406, it may be determined that content recommendation is to be performed for the current search in response to determining that there is at least one feature of the plurality of features that matches the preset target feature.

In the above manner, the user portraits can be determined from multiple dimensions based on the identification information, thereby further determining whether a scene that triggered the content recommendation is appropriate.

In some embodiments, the identification information may include information related to the user performing the current retrieval. For example, the identification information may include account information of the user in the application, account registration time, account level, and the like.

In some embodiments, the preset target features may be derived based on historical statistics. For example, the preset target features may be counted based on the Grouping Sets function in SQL (Structured Query Language ) using historical statistics. That is, it may be reflected via historical statistics what features the user is more likely to respond to the content recommendation. For example, the preset target feature may include a newly registered account, so if it is determined based on the identification information that the user who performs the current search has just recently registered an account, it may be determined that the user has a feature matching the preset target feature, whereby it may be determined that content recommendation is to be performed for the current search.

In some embodiments, the preset target features may be configured to train the conversion rate determination model as training data. The conversion rate determination model may be used to determine, from the entered features, a probability that a user having the features will respond to the content recommendation based on the associated information.

Therefore, the conversion rate determination model after training can quickly and simply judge whether the scene of content recommendation is suitable for triggering the user, so that the effectiveness of the content recommendation is improved, and the effect of the content recommendation is improved conveniently.

Fig. 5 shows a schematic diagram of the structure of a conversion determination model according to an embodiment of the present disclosure.

As shown in fig. 5, the conversion determination model 500 may take the form of a multi-layer MLP model or other network architecture. For example, the conversion determination model 500 may include a feature input layer 510, an encoding layer 520, a full-link hidden layer 530, and an LR (Logistic Regression ) output layer 540.

As an example, the code to build and train the conversion determination model is shown below. It is to be understood that the various aspects of the present disclosure are not limited to the specific examples in the following codes.

Fig. 6 illustrates a flowchart of a content recommendation method according to another embodiment of the present disclosure.

As shown in fig. 6, the content recommendation method 600 according to an embodiment of the present disclosure may include steps S602, S604, S606, S608, S610, and S612.

Steps S602 to S608 may be the same as steps S202 to S208 described in connection with fig. 2.

Further, at step S610, it may be determined whether the associated information is clicked and the content recommendation is responded to.

In step S612, preset keywords and/or associated information may be adjusted based on whether the associated information is clicked and whether the content recommendation is responded to.

Accordingly, the content recommendation policy can be dynamically optimized by taking the click of the associated information and the response of the content recommendation as feedback information, thereby facilitating the promotion of the effect of the content recommendation.

In some embodiments, for preset keywords that form more clicks but less responses (i.e., conversions), they may be appropriately negated. For the preset keywords with less clicks and more responses, word expansion can be performed, and the expanded preset keywords can be added in time.

According to an embodiment of the present disclosure, there is also provided a content recommendation apparatus.

Fig. 7 shows a block diagram of a content recommendation device according to one embodiment of the present disclosure.

As shown in fig. 7, the content recommendation device 700 includes a keyword generation module 702, a match determination module 704, a content recommendation determination module 706, and a presentation module 708.

The keyword generation module 702 is configured to generate preset keywords for making content recommendations.

The match determination module 704 is configured to determine whether the search keyword matches a preset keyword.

The content recommendation determination module 706 is configured to determine whether to recommend content for the current search based on the identification information in response to determining that the search keyword matches the preset keyword.

The presentation module 708 is configured to present association information associated to the content recommendation in response to determining that the content recommendation is to be made for the current retrieval.

The keyword generation module 702, the match determination module 704, the content recommendation determination module 706, and the presentation module 708 may correspond to steps S202, S204, S206, and S208, respectively, as shown in fig. 2. Accordingly, details of various aspects thereof are not described herein.

Fig. 8 illustrates a block diagram of a content recommendation device according to another embodiment of the present disclosure.

As shown in fig. 8, the content recommendation device 800 may include a keyword generation module 802, a match determination module 804, a content recommendation determination module 806, and a presentation module 808. The above-described modules may be the same as the keyword generation module 702, the match determination module 704, the content recommendation determination module 706, and the presentation module 708 as shown in fig. 7.

In some embodiments, keyword generation module 802 may include: a first generation module 802a configured to generate seed keywords based on the large language model; a second generation module 802b configured to expand the seed keywords to generate candidate keywords; and a selection module 802c configured to select at least a part of the candidate keywords satisfying a predetermined condition as a preset keyword.

In some embodiments, the preset keywords may be assigned corresponding keyword identifications.

In some embodiments, the content recommendation determination module 806 may include: a feature determination module 806a configured to determine a plurality of features of the user performing the current retrieval based on the identification information; a first determining module 806b configured to determine whether there is at least one feature of the plurality of features that matches a preset target feature; and a second determining module 806c configured to determine that content recommendation is to be made for the current retrieval in response to determining that there is at least one feature of the plurality of features that matches the preset target feature.

In some embodiments, the preset target feature may be configured to train a conversion rate determination model as training data, which may be used to determine a probability that a user having the feature responds to the content recommendation based on the associated information from the input feature.

In some embodiments, the content recommendation device 800 may further include: a click and response determination module 810 configured to determine whether the associated information is clicked and whether the content recommendation is responded; and an adjustment module 812 configured to adjust the preset keywords and/or the associated information based on whether the associated information is clicked and whether the content recommendation is responded to.

There is also provided, in accordance with an embodiment of the present disclosure, an electronic device including at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

There is also provided, in accordance with an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described above.

There is also provided, in accordance with an embodiment of the present disclosure, a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method as described above.

Referring to fig. 9, a block diagram of an electronic device 900 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 9, the electronic device 900 includes a computing unit 901 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.

A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 may be any type of device capable of inputting information to the electronic device 900, the input unit 906 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may 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 907 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 908 may include, but is not limited to, magnetic disks, optical disks. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized 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 901 performs the respective methods and processes described above. For example, in some embodiments, the method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may 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), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute 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.

In the context of this disclosure, a machine-readable medium may 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. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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.

To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, 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: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of 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 the disclosure.

Claims (15)

1. A content recommendation method, comprising:

generating preset keywords for content recommendation;

Determining whether the search keywords are matched with the preset keywords;

in response to determining that the search keyword matches the preset keyword, determining whether to make the content recommendation for the current search based on the identification information; and

In response to determining that the content recommendation is to be made for the current retrieval, association information associated with the content recommendation is presented.

2. The method of claim 1, wherein the generating the preset keyword for making the content recommendation comprises:

generating seed keywords based on the large language model;

Expanding the seed keywords to generate candidate keywords; and

And selecting at least one part of the candidate keywords meeting a preset condition as the preset keywords.

3. The method according to claim 1 or 2, wherein the preset keywords are assigned corresponding keyword identifications.

4. A method according to any one of claims 1 to 3, wherein the determining whether to make the content recommendation for a current search based on the identification information comprises:

determining a plurality of characteristics of a user performing current retrieval based on the identification information;

determining whether at least one feature matched with a preset target feature exists in the plurality of features; and

In response to determining that there is at least one feature of the plurality of features that matches the preset target feature, determining that the content recommendation is to be made for a current search.

5. The method of claim 4, wherein the preset target feature is configured to train a conversion rate determination model as training data, the conversion rate determination model to determine a probability that a user having the feature responds to the content recommendation based on the association information from the input feature.

6. The method of any one of claims 1 to 5, further comprising:

determining whether the associated information is clicked and whether the content recommendation is responded; and

And adjusting the preset keywords and/or the associated information based on whether the associated information is clicked and whether the content recommendation is responded.

7. A content recommendation device, comprising:

the keyword generation module is configured to generate preset keywords for content recommendation;

the matching determining module is configured to determine whether the search keyword is matched with the preset keyword;

a content recommendation determination module configured to determine whether to make the content recommendation for a current search based on identification information in response to determining that the search keyword matches the preset keyword; and

And a presentation module configured to present association information associated with the content recommendation in response to determining that the content recommendation is to be made for a current search.

8. The apparatus of claim 7, wherein the keyword generation module comprises:

a first generation module configured to generate seed keywords based on the large language model;

The second generation module is configured to expand the seed keywords to generate candidate keywords; and

And the selection module is configured to select at least one part of the candidate keywords meeting a preset condition as the preset keywords.

9. The apparatus according to claim 7 or 8, wherein the preset keywords are assigned corresponding keyword identifications.

10. The apparatus of any of claims 7 to 9, wherein the content recommendation determination module comprises:

a feature determination module configured to determine a plurality of features of a user performing a current search based on the identification information;

A first determining module configured to determine whether there is at least one feature of the plurality of features that matches a preset target feature; and

A second determination module configured to determine that the content recommendation is to be made for a current search in response to determining that there is at least one feature of the plurality of features that matches the preset target feature.

11. The apparatus of claim 10, wherein the preset target feature is configured to train a conversion rate determination model as training data, the conversion rate determination model to determine a probability that a user having the feature responds to the content recommendation based on the association information from the input feature.

12. The apparatus of any of claims 7 to 11, further comprising:

A click and response determination module configured to determine whether the associated information is clicked and whether the content recommendation is responded; and

And the adjusting module is configured to adjust the preset keywords and/or the associated information based on whether the associated information is clicked and whether the content recommendation is responded.

13. An electronic device, comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor;

Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-6.

14. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to any of claims 1-6.