Content recommendation method and device, electronic equipment, readable storage medium and computer program product

Abstract

The invention provides a content recommendation method and device, electronic equipment, a readable storage medium and a computer program product, and relates to the field of artificial intelligence, in particular to the field of intelligent recommendation. According to the implementation scheme, a user intention vector is obtained based on user input information; the similarity between the user intention vector and a content vector in a content vector database is calculated, and the content vector database comprises the content vector corresponding to recommended content; in response to determining that at least one similarity meeting a similarity threshold exists, obtaining at least one first recommendation content; based on content performance information of the at least one first recommendation content, obtaining a content score of each first recommendation content in the at least one first recommendation content, the content performance information recording historical information of the at least one first recommendation content; and obtaining at least one second recommendation content corresponding to the user input information based on the content score.

Description

Technical Field

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

[0002] With the rapid development of information technology, how to obtain content that truly meets users' needs from massive amounts of information has become a concern for users. The development of intelligent technology provides solutions for users to obtain personalized content from massive amounts of information.

[0003] Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level 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 computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions. Summary of the invention

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

[0005] According to one aspect of the present disclosure, a content recommendation method is provided, comprising: obtaining a user intention vector based on user input information; calculating the similarity between the user intention vector and a content vector in a content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; in response to determining that there is at least one similarity that satisfies a similarity threshold, obtaining at least one first recommended content corresponding to the at least one similarity; obtaining a content score for each first recommended content in the at least one first recommended content based on content performance information of each first recommended content in the at least one first recommended content, wherein the content performance information records historical information of each first recommended content in the at least one first recommended content; and obtaining at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content.

[0006] According to a second aspect of the present disclosure, a content recommendation device is provided, comprising: a user intention vector acquisition module, used to obtain a user intention vector based on user input information; a similarity calculation module, used to calculate the similarity between the user intention vector and a content vector in a content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; a first recommended content acquisition module, used to obtain at least one first recommended content corresponding to the at least one similarity in response to determining that there is at least one similarity that satisfies a similarity threshold; a content score calculation module, used to obtain a content score of each first recommended content in the at least one first recommended content based on content performance information of each first recommended content in the at least one first recommended content, wherein the content performance information records historical information of each first recommended content in the at least one first recommended content; and a second recommended content acquisition module, used to obtain at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content.

[0007] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned content recommendation method.

[0008] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor implements the content recommendation method as described above.

[0009] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the above-mentioned content recommendation method when executed by a processor.

[0010] According to one or more embodiments of the present disclosure, content related to user input information is obtained through a content vector database, and the matched content is scored based on content performance information, and recommended content corresponding to the user input information is obtained according to the scoring results, thereby providing an efficient content recommendation method and device.

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

[0012] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

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

[0015] Figure 3 Shown according to Figure 2 The flowchart of the method for obtaining the content score of each first recommended content in the at least one first recommended content based on the content performance information of each first recommended content in the at least one first recommended content in step S208 shown;

[0016] Figure 4 A block diagram of a content recommendation device according to an embodiment of the present disclosure is shown;

[0017] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0019] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0020] The terms used in the description of various examples in this disclosure 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 can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0021] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0022] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0023] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the content recommendation method to be performed.

[0024] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0025] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0026] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to implement content recommendations. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0027] 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, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game 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.

[0028] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a 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.

[0029] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0030] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0031] 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.

[0032] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with 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. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0033] 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 the databases 130 may be used to store information such as audio files and video files. The databases 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. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0034] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0035] Figure 1 The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0036] Figure 2 2 is a flowchart showing a content recommendation method 200 according to an embodiment of the present disclosure. Figure 2 As shown, the content recommendation method may include: step S202, obtaining a user intention vector based on user input information; step S204, calculating the similarity between the user intention vector and the content vector in the content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; step S206, in response to determining that there is at least one similarity that satisfies the similarity threshold, obtaining at least one first recommended content corresponding to the at least one similarity; step S208, based on the content performance information of each first recommended content in the at least one first recommended content, obtaining a content score for each first recommended content in the at least one first recommended content, wherein the content performance information records the historical information of each first recommended content in the at least one first recommended content; and step S210, based on the content score of each first recommended content in the at least one first recommended content, obtaining at least one second recommended content corresponding to the user input information.

[0037] In some embodiments, in step S202, the user input information includes question information input by the user, associative word information associated with the user input information, and may also be text prompt word information input by the user during the interaction with the model; the user request information is a query involving a broad topic, and the corresponding generated content may also be tens of thousands. The user can adjust the prompt words according to needs to adapt to different scenarios and tasks. If the user has specific requirements for the format of the generated content, it can be clearly stated in the prompt words, such as "Please recommend five books on architecture"; the prompt word information and associative word information can guide the model to generate specific outputs and help users obtain required content more efficiently.

[0038] In some embodiments, the vectors stored in the content vector database are derived from vectorization of the recommended content, and the recommended content includes recommended content in text, voice, image, video, and other formats corresponding to the user input information.

[0039] In some embodiments, the calculation method of the similarity between the user intention vector and the content vector in the content vector database includes Euclidean distance calculation method, cosine similarity and algorithm, and Pearson correlation coefficient calculation method, etc., and an appropriate similarity calculation method can be selected according to different application scenarios.

[0040] In the content recommendation method according to the embodiment of the present disclosure, content related to the user input information is obtained through the content vector database, and the matched content is scored based on the content performance information. According to the scoring result, recommended content corresponding to the user input information is obtained, thereby effectively improving the adaptability of the content and the user input information.

[0041] Figure 3 Shown according to Figure 2 In step S208, a method flow chart 300 is shown in which a content score of each first recommended content in the at least one first recommended content is obtained based on the content performance information of each first recommended content in the at least one first recommended content; Figure 3 As shown, the method of obtaining the content score of each first recommended content in the at least one first recommended content based on the content performance information of each first recommended content in the at least one first recommended content includes: step S302, for each first recommended content in the at least one first recommended content, based on the content performance information of the first recommended content, obtaining the user behavior data of the first recommended content, wherein the user behavior data includes content clicks and content interactions, wherein the content clicks are the number of times the corresponding first recommended content is clicked by users, and the content interaction is the sum of the number of user interaction behaviors of the corresponding first recommended content; and step S304, based on the user behavior data of the first recommended content, obtaining the content score of the first recommended content.

[0042] In this embodiment, the content score of each of the at least one first recommended content is calculated by effectively reflecting the content clicks and content interactions converted by each of the at least one first recommended content, and feedback is used to adjust the content recommendation, thereby effectively improving the attractiveness of the recommended content to users.

[0043] In some embodiments, the content click volume is the number of times a user clicks on a link, button or other user behavior that can access each first recommended content in the at least one first recommended content, wherein the content click volume reflects the attractiveness of each first recommended content in the at least one first recommended content to the user; the user interaction behavior includes downloading, copying, sharing, liking, collecting and other user interaction behaviors that can reflect the user's interaction with each first recommended content in the at least one first recommended content, indicating the user's recognition of the quality or matching degree of each first recommended content in the at least one first recommended content after clicking into each first recommended content in the at least one first recommended content.

[0044] It should be noted that in the embodiments of the present invention, the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of the user behavior data involved all comply with the provisions of relevant laws and regulations, are used for legal purposes, and do not violate public order and good morals.

[0045] In some embodiments, obtaining the content score of the first recommended content based on the user behavior data of the first recommended content includes: calculating the sum of first logarithmic values ​​of content clicks and content interactions of the first recommended content, and using the sum of the first logarithmic values ​​as the content score of the first recommended content.

[0046] In some embodiments, the historical information of the first recommended content includes non-linearly growing user behavior data, which may be due to competitors, market environment or other data that may cause periodic exponential growth of user behavior data. In this embodiment, through logarithmic operation, the impact of the possible exponential growth of content clicks and content interactions on the recommended content is stabilized, the accuracy of content score calculation is improved, and thus the accuracy of content recommendation is improved.

[0047] In some embodiments, the calculation of the sum of the first logarithmic values ​​of the content click volume and the content interaction volume of the first recommended content includes: normalizing the content interaction volume and the content click volume of the first recommended content to respectively obtain the normalized value of the content interaction volume and the normalized value of the content click volume of the first recommended content; and calculating the sum of the second logarithmic values ​​of the normalized value of the content interaction volume and the normalized value of the content click volume of the first recommended content, and using the sum of the second logarithmic values ​​as the sum of the first logarithmic values.

[0048] Exemplarily, the content score of the first recommended content is calculated according to the following formula:

[0049] Score = M × log a (1+X)+N×log b (1+Y)

[0050] Among them, Score represents the content score of the first recommended content, X represents the normalized value of the content clicks of the first recommended content, Y represents the normalized value of the content interaction of the first recommended content, a and b are the bases of the logarithmic operations of the content clicks of the first recommended content and the content interaction of the first recommended content, respectively, and M and N represent the weights of X and Y, respectively.

[0051] In this embodiment, the content score is calculated by normalizing the content click volume and the content interaction volume, thereby optimizing the problem of calculation overflow caused by excessive content click volume or content interaction volume.

[0052] In this embodiment, the values ​​of M and N are adjusted according to the user behavior data in the actual application scenario. For example, for application scenarios with a large amount of content interaction, the value of M is 0.2 and the value of N is 0.3. These weights can also be optimized based on experimental results or historical data analysis to achieve better content recommendation effects.

[0053] In this embodiment, the calculation method of the normalized values ​​of the content click volume and the content interaction volume can be a calculation method based on maximum-minimum normalization, or other calculation methods that can effectively prevent the content click volume and the content interaction volume from being too large and causing calculation overflow.

[0054] In some embodiments, the method also includes: for each first recommended content among the at least one first recommended content, based on the content performance information of the first recommended content, obtaining content recommendation data of the first recommended content, wherein the content recommendation data includes a content recommendation amount, and the content recommendation amount is the number of times the first recommended content is recommended; calculating the content conversion rate of the recommended content, wherein the content conversion rate is the ratio of the content interaction amount to the content click amount of the recommended content; based on the content recommendation amount of the first recommended content, normalizing the content conversion rate of the recommended content to obtain a normalized value of the content conversion rate of the recommended content; and adding the sum of the first logarithmic values ​​of the recommended content to the normalized value of the content conversion rate to obtain a first numerical sum, and using the first numerical sum as the content score of the recommended content.

[0055] Exemplarily, the content score of the first recommended content is calculated according to the following formula:

[0056] Score = M × log a (1+X)+N×log b (1+Y)+Dvall / ((D+1 / 10 α ))

[0057] Among them, Score represents the content score of the first recommended content, X represents the normalized value of the content clicks of the first recommended content, Y represents the normalized value of the content interaction of the first recommended content, a and b are the bases of the logarithmic operations of the content clicks of the first recommended content and the content interaction of the first recommended content, respectively, D represents the content recommendation volume of the first recommended content, Dvall represents the user conversion rate of the first recommended content, M and N represent the weights of X and Y respectively, α represents the normalization factor of the content recommendation volume of the first recommended content, and the calculation formula of the user conversion rate of the first recommended content is Dvall=Y / X.

[0058] Conversion rate is an important indicator for measuring the effectiveness of content recommendation. Content with a high conversion rate means that the recommended content is more in line with the needs and interests of users. In this embodiment, through logarithmic operations, the impact of possible exponential growth in content clicks and content interactions on recommended content is smoothed, and the accuracy of content ratings is further improved through the coordinated calculation of user conversion rate and content recommendation volume.

[0059] In some embodiments, in response to the content recommendation amount of each first recommended content in the at least one first recommended content being zero, a normalization factor α of the content recommendation amount is used to ensure the stability of the content score calculation, wherein the value of the normalization factor α of the content recommendation amount is adjusted according to the actual application scenario, and exemplarily, the value of α is 0.6.

[0060] In some embodiments, the method also includes: in response to determining that the user input information contains timeliness information, obtaining the release date of each first recommended content in the at least one first recommended content and the input date of the user input information; and, for each first recommended content in the at least one first recommended content, weighting the date interval between the release date of the first recommended content and the input date to obtain a weighted processing value of the date interval of the first recommended content; and adding the first numerical sum of the first recommended content and the weighted processing value of the date interval to obtain a second numerical sum, and using the second numerical sum as the content score of the first recommended content.

[0061] Exemplarily, the content score of the first recommended content is calculated according to the following formula:

[0062]

[0063] Among them, Score represents the content score of the first recommended content, X represents the normalized value of the content click volume of the first recommended content, Y represents the normalized value of the content interaction volume of the first recommended content, a and b are the bases of the logarithmic operation of the content click volume and the content interaction volume of the first recommended content, respectively, Z represents the date interval between the release date of the first recommended content and the input date of the user input information, D represents the normalized value of the content recommendation volume of the first recommended content, Dvall represents the user conversion rate of the first recommended content, M, N and L represent the weights of X, Y and Z respectively, α represents the normalization factor of the content recommendation volume of the first recommended content, wherein the calculation formula for the user conversion rate of the first recommended content is Dvall=Y / X.

[0064] In this embodiment, L may be calculated by a maximum-minimum normalization method or other normalization methods.

[0065] In this embodiment, for user input information with time-sensitive information, the impact of possible exponential growth in content clicks and content interactions on recommended content will be smoothed, and the accuracy of content ratings will be further improved through the coordinated calculation of user conversion rate and content recommendation volume. At the same time, the impact of the release time of the first recommended content on the content rating is taken into account, thereby further improving the adaptability of the recommended content to the user input information.

[0066] In some embodiments, obtaining at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content includes: taking one or more first recommended contents in the at least one first recommended content whose content score satisfies a content score threshold as the at least one second recommended content.

[0067] In this embodiment, in response to determining that there is no content recommendation quantity label in the user input information, the at least one first recommended content is further filtered by setting a content score threshold to obtain the at least one second recommended content, thereby effectively improving the adaptability of the content recommendation.

[0068] In some embodiments, obtaining at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content includes: in response to determining that there is a content recommendation quantity label in the user input information, sorting the content score of each first recommended content in the at least one first recommended content, wherein the content recommendation quantity label indicates the quantity of the at least one second recommended content that the user wants to obtain; and obtaining the at least one second recommended content corresponding to the user input information based on the sorting order of the content scores of each first recommended content in the at least one first recommended content and the content recommendation quantity label.

[0069] In this embodiment, in response to the user's requirement on the quantity of recommended content, the at least one second recommended content is further filtered based on the content recommendation quantity label to obtain recommended content that meets the user's required quantity, thereby effectively improving the adaptability of the recommended content to the user input information.

[0070] Figure 4 4 is a block diagram showing a content recommendation device 400 according to an embodiment of the present disclosure. In some embodiments, Figure 4 As shown, the content recommendation device includes: a user intention vector acquisition module 402, which is used to obtain a user intention vector based on user input information; a similarity calculation module 404, which is used to calculate the similarity between the user intention vector and the content vector in the content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; a first recommended content acquisition module 406, which is used to obtain at least one first recommended content corresponding to the at least one similarity in response to determining that there is at least one similarity that meets the similarity threshold; a content score calculation module 408, which is used to obtain a content score of each first recommended content in the at least one first recommended content based on content performance information of each first recommended content in the at least one first recommended content, wherein the content performance information records the historical information of the corresponding first recommended content; and a second recommended content acquisition module 410, which is used to obtain at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content.

[0071] It should be noted that Figure 4 The various modules of the apparatus 400 shown in FIG. Figure 2 The steps in the method 200 described above correspond to each other. Therefore, the operations, features and advantages described above for the method 200 are also applicable to the device 400 and the modules and units included therein. For the sake of brevity, some operations, features and advantages are not repeated here.

[0072] According to an embodiment of the present disclosure, an electronic device, a readable storage medium and a computer program product are also provided.

[0073] refer to Figure 5 , a block diagram of an electronic device 500 that can be used as a server or client of the present disclosure will now be described, which 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 laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, 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 required herein.

[0074] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0075] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500. The input unit 506 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 507 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0076] The computing unit 501 may be a variety of general and / or special 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, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0077] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0079] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0081] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0082] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0083] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0084] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A content recommendation method, comprising: Based on the user input information, obtain the user intention vector; Calculating the similarity between the user intention vector and a content vector in a content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; In response to determining that there is at least one similarity that satisfies the similarity threshold, obtaining at least one first recommended content corresponding to the at least one similarity; Obtaining a content score for each of the at least one first recommended content based on content performance information of each of the at least one first recommended content, wherein the content performance information records historical information of the corresponding first recommended content; and At least one second recommended content corresponding to the user input information is obtained based on the content score of each first recommended content in the at least one first recommended content.

2. The content recommendation method according to claim 1, wherein: The obtaining, based on the content performance information of each first recommended content in the at least one first recommended content, a content score of each first recommended content in the at least one first recommended content comprises: For each first recommended content in the at least one first recommended content, Based on the content performance information of the first recommended content, obtaining user behavior data of the first recommended content, wherein the user behavior data includes content clicks and content interactions, wherein the content clicks are the number of times the corresponding first recommended content is clicked by users, and the content interactions are the sum of the number of user interaction behaviors of the corresponding first recommended content; and Based on the user behavior data of the first recommended content, a content score of the first recommended content is obtained.

3. The content recommendation method according to claim 2, wherein: The obtaining of a content score of the first recommended content based on the user behavior data of the first recommended content includes: The sum of first pair values ​​of the content click volume and the content interaction volume of the first recommended content is calculated, and the sum of the first pair values ​​is used as the content score of the first recommended content.

4. The content recommendation method according to claim 3, wherein: The calculating the sum of the first logarithmic value of the content click volume and the content interaction volume of the first recommended content includes: Normalizing the content interaction amount and the content click amount of the first recommended content to obtain a normalized value of the content interaction amount and a normalized value of the content click amount of the first recommended content, respectively; and The sum of the second logarithmic values ​​of the normalized value of the content interaction amount and the normalized value of the content click amount of the first recommended content is calculated, and the sum of the second logarithmic values ​​is used as the sum of the first logarithmic values.

5. The content recommendation method according to claim 3, wherein: The method further comprises: For each first recommended content in the at least one first recommended content, Based on the content performance information of the first recommended content, obtaining content recommendation data of the first recommended content, wherein the content recommendation data includes a content recommendation amount, and the content recommendation amount is the number of times the first recommended content is recommended; Calculating the content conversion rate of the recommended content, wherein the content conversion rate is the ratio of the content interaction volume to the content click volume of the recommended content; Based on the content recommendation amount of the first recommended content, normalizing the content conversion rate of the recommended content to obtain a normalized value of the content conversion rate of the recommended content; and The sum of the first logarithmic values ​​of the recommended content and the normalized value of the content conversion rate are added to obtain a first numerical sum, and the first numerical sum is used as the content score of the recommended content.

6. The content recommendation method according to claim 5, wherein: The method further comprises: In response to determining that the user input information has time validity information, obtaining a release date of each first recommended content in the at least one first recommended content and an input date of the user input information; and, For each first recommended content in the at least one first recommended content, Performing weighted processing on the date interval between the release date of the first recommended content and the input date to obtain a weighted processing value of the date interval of the first recommended content; and The first numerical sum of the first recommended content and the weighted value of the date interval are added to obtain a second numerical sum, and the second numerical sum is used as the content score of the first recommended content.

7. The content recommendation method according to any one of claims 4 to 6, wherein: The obtaining, based on the content score of each first recommended content in the at least one first recommended content, at least one second recommended content corresponding to the user input information comprises: One or more first recommended contents whose content scores satisfy a content score threshold among the at least one first recommended content are used as the at least one second recommended content.

8. The content recommendation method according to any one of claims 4 to 6, wherein: The obtaining, based on the content score of each first recommended content in the at least one first recommended content, at least one second recommended content corresponding to the user input information comprises: In response to determining that there is a content recommendation quantity tag in the user input information, sorting the content score of each first recommended content in the at least one first recommended content, wherein the content recommendation quantity tag indicates the quantity of the at least one second recommended content that the user wants to obtain; and Based on the sorting order of the content score of each first recommended content in the at least one first recommended content and the content recommendation quantity label, the at least one second recommended content corresponding to the user input information is obtained.

9. A content recommendation device, comprising: A user intention vector acquisition module, used to obtain a user intention vector based on user input information; A similarity calculation module, used to calculate the similarity between the user intention vector and the content vector in the content vector database, wherein the content vector database includes the content vector corresponding to the recommended content; A first recommended content acquisition module, configured to, in response to determining that there is at least one similarity satisfying a similarity threshold, acquire at least one first recommended content corresponding to the at least one similarity; a content score calculation module, configured to obtain a content score of each first recommended content in the at least one first recommended content based on content performance information of each first recommended content in the at least one first recommended content, wherein the content performance information records historical information of the corresponding first recommended content; and The second recommended content acquisition module is used to obtain at least one second recommended content corresponding to the user input information based on the content score of each first recommended content in the at least one first recommended content.

10. The content recommendation device according to claim 9, wherein: The content score calculation module further includes: For each first recommended content in the at least one first recommended content, A submodule for obtaining user behavior data of the first recommended content based on the content performance information of the first recommended content, wherein the user behavior data includes content clicks and content interactions, wherein the content clicks are the number of times the corresponding first recommended content is clicked by users, and the content interactions are the sum of the number of user interaction behaviors of the corresponding first recommended content; and A submodule for obtaining a content score of the first recommended content based on the user behavior data of the first recommended content.

11. The content recommendation device according to claim 10, wherein: The submodule for obtaining the content score of the first recommended content based on the user behavior data of the first recommended content further includes: The first unit is used to calculate the sum of first logarithmic values ​​of the content click volume and the content interaction volume of the first recommended content, and use the sum of the first logarithmic values ​​as the content score of the first recommended content.

12. The content recommendation device according to claim 11, wherein: The first unit further comprises: a subunit for normalizing the content interaction amount and the content click amount of the first recommended content to obtain a normalized value of the content interaction amount and a normalized value of the content click amount of the first recommended content; and Used to calculate the sum of the second logarithmic values ​​of the normalized value of the content interaction amount and the normalized value of the content click amount of the first recommended content, and use the sum of the second logarithmic values ​​as a subunit of the sum of the first logarithmic values.

13. The content recommendation device according to claim 11, wherein: The first unit further comprises: For each first recommended content in the at least one first recommended content, A subunit for obtaining content recommendation data in the first recommended content based on the content performance information of the first recommended content, wherein the content recommendation data includes a content recommendation amount, which is the number of times the first recommended content is recommended; A subunit for calculating a content conversion rate of the recommended content, wherein the content conversion rate is a ratio of the content interaction volume to the content click volume of the recommended content; a subunit for normalizing the content conversion rate of the recommended content based on the content recommendation amount of the first recommended content to obtain a normalized value of the content conversion rate of the recommended content; and The method is used to add the sum of the first logarithmic values ​​of the recommended content and the normalized value of the content conversion rate to obtain a first numerical sum, and use the first numerical sum as a subunit of the content score of the recommended content.

14. The content recommendation device according to claim 13, wherein: The first unit further comprises: a subunit configured to, in response to determining that the user input information has time validity information, obtain a release date of each first recommended content in the at least one first recommended content and an input date of the user input information; For each first recommended content in the at least one first recommended content, A subunit configured to perform weighted processing on the date interval between the release date of the first recommended content and the input date to obtain a weighted processing value of the date interval of the first recommended content; and The method is used to add the first numerical value sum of the first recommended content and the weighted processing value of the date interval to obtain a second numerical value sum, and use the second numerical value sum as a subunit of the content score of the first recommended content.

15. The content recommendation device according to any one of claims 12 to 14, wherein: The second recommended content acquisition module further includes: A submodule for using one or more first recommended contents whose content scores satisfy a content score threshold in the at least one first recommended content as the at least one second recommended content.

16. The content recommendation device according to any one of claims 12 to 14, wherein: The second recommended content acquisition module further includes: a submodule for sorting the content score of each of the at least one first recommended content in response to determining that there is a content recommendation quantity tag in the user input information, wherein the content recommendation quantity tag indicates the quantity of the at least one second recommended content that the user wants to obtain; and A submodule for obtaining the at least one second recommended content corresponding to the user input information based on the sorting order of the content score of each first recommended content in the at least one first recommended content and the content recommendation quantity label.

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

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

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

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