CN114791982B - Object recommendation method and device - Google Patents
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Abstract
The disclosure provides an object recommendation method and device, relates to the technical field of computers, and particularly relates to the technical field of artificial intelligence-based recommendation. The implementation scheme is as follows: obtaining a first user subset corresponding to an object to be recommended from a user set, wherein the influence of each user in the first user subset is greater than the influence of a first user in the user set, which is different from each user in the first user subset; recommending the object to each user in the first subset of users to obtain a first subset of the first set of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and recommending the object to a target user in the user set based on the first subset, the target user being distinct from each user in the first subset of users.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for object recommendation, an electronic device, a computer-readable storage medium, and a computer program product.
Background
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. 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, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
Artificial intelligence based recommendation techniques have penetrated into various fields. The object recommendation method based on artificial intelligence recommends objects conforming to the preferences of users to the users by predicting the preferences of the users to the objects.
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 acknowledged in any prior art, unless otherwise indicated.
Disclosure of Invention
The present disclosure provides an object recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.
According to an aspect of the present disclosure, there is provided an object recommendation method including: obtaining a first user subset corresponding to an object to be recommended from a user set, wherein the influence of each user in the first user subset is greater than the influence of a first user in the user set different from each user in the first user subset, wherein each user in the user set has a corresponding user subset, and the influence indicates the possibility that each user in a relevant user subset of the corresponding user has a preference for the object preferred by the corresponding user; recommending the object to each user in a first subset of users to obtain a first subset of the first set of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and determining whether to recommend the object to a target user in the set of users based on the first subset, the target user being distinct from each user in the first subset of users.
According to another aspect of the present disclosure, there is provided an object recommendation apparatus including: a first user set obtaining unit, configured to obtain, from a user set, a first user subset corresponding to an object to be recommended, an influence of each user in the first user subset being greater than an influence of a first user in the user set different from each user in the first user subset, wherein each user in the user set has a corresponding user subset, and the influence indicates a possibility that each user in a relevant user subset of the corresponding user has a preference for an object preferred by the corresponding user; a first recommending unit configured to recommend the object to each user in a first subset of users to obtain a first subset of the first set of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and a second recommending unit configured to determine whether to recommend the object to a target user in the user set based on the first subset, the target user being different from each user in the first subset of users.
According to another aspect of the present disclosure, there is provided 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 a method according to embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to the embodiments of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method of embodiments of the present disclosure.
According to one or more embodiments of the disclosure, the information cocoon room of the target user caused by the object recommended to the target user can be avoided, and meanwhile, the object recommended to the target user can be the object preferred by the target user, so that the user experience is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers 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, according to an embodiment of the present disclosure;
FIG. 2 shows a flow diagram of an object recommendation method according to an embodiment of the present disclosure;
fig. 3 is a flowchart illustrating a process of obtaining a first user subset corresponding to an object to be recommended from a user set in an object recommendation method according to an embodiment of the present disclosure;
FIG. 4 shows a flowchart of a process in an object recommendation method for determining whether to recommend an object to a target user in a set of users based on a first subset, according to an embodiment of the present disclosure;
FIG. 5 illustrates a flow diagram of a process in which a predicted preference of a target user for an object based on a first subset in an object recommendation method according to an embodiment of the disclosure may be implemented;
FIG. 6 illustrates a flow diagram of a process in which obtaining a vector representation of an object based on a vector representation of each user in a first subset in an object recommendation method according to an embodiment of the disclosure may be implemented;
FIG. 7 illustrates a flow diagram of a process by which obtaining prediction preferences based on vector representations of objects and vector representations of target users in an object recommendation method according to embodiments of the disclosure may be implemented;
fig. 8 illustrates a block diagram of an object recommendation apparatus according to an 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 with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, it will 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. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, while in some cases they may refer to different instances based on the context of the description.
The terminology used in the description of the various described 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, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
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 embodiments 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 the execution of the object recommendation method.
In some embodiments, the 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, which may be executed by one or more processors. A user operating a client device 101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood 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 use client devices 101, 102, 103, 104, 105, and/or 106 to receive the recommended object. 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 any number of client devices may be supported by the present disclosure.
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 involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the 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. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.
In some implementations, the server 120 can include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the 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 embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the conventional physical host and Virtual Private Server (VPS) 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 the databases 130 may be used to store information such as audio files and video files. The 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. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the databases in response to the commands.
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 application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with this disclosure.
According to an aspect of the present disclosure, an object recommendation method is provided. As shown in fig. 2, an object recommendation method 200 according to some embodiments of the present disclosure includes:
step S210: obtaining a first subset of users corresponding to an object to be recommended from a set of users, an influence of each user in the first subset of users being greater than an influence of a first user in the set of users different from each user in the first subset of users, wherein each user in the set of users has a corresponding subset of users, the influence indicating a likelihood that each user in a relevant subset of users of the corresponding user has a preference for the object preferred by the corresponding user;
step S220: recommending the object to each user in a first subset of users to obtain a first subset of the first set of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and
step S230: based on the first subset, determining whether to recommend the object to a target user in the set of users, the target user being distinct from each user in the first subset of users.
The first user subset corresponding to the object to be recommended is obtained from the user set, and since the influence of each user in the first user subset is greater than the influence of the first user in the user set different from each user in the first user subset, the obtained users in the first user subset are users with greater influence, that is, when the object preferred by the user is recommended to each user in the relevant user subset of the user, the possibility that the object preferred by the user is preferred by the user in the corresponding user subset is greater. When the object to be recommended is recommended to each user in the first user subset, when a user in the first user subset prefers the object, the probability that the user in the related user subset prefers the object is high. By obtaining a first subset of the first subset of users having a preference for the object, and recommending the object to the target user based on the first subset. Because the probability that the object is preferred by the related user subsets of the users in the first subset is high, whether the object is recommended to the target user is judged through the first subset, so that the judgment result is more accurate, and the user experience of the obtained target user is improved.
In the related art, the relevance of a user to a candidate object is determined based on the historical behavior of the user, and it is determined whether to recommend the candidate object to the user based on the relevance. The method of obtaining the correlation between the candidate object and the user is, for example, to calculate the similarity between the candidate object and the object preferred by the user indicated by the user history behavior, and to recommend the object using the similarity as an evaluation index of the correlation. And the object recommended to the user is an object related to the interest point of the user, so that the information cocoon room is formed. In order to recommend diverse objects, a policy targeting irrelevance is often used to adjust the issued objects, for example, irrelevant objects are randomly pushed, so that the recommended objects are not good and the user experience is damaged.
In the embodiment according to the disclosure, whether the object is recommended to the target user is judged without taking the correlation as an index, so that the information cocoon room of the target user caused by the object recommended to the target object can be avoided. Meanwhile, the object recommended to the target object is not an irrelevant object pushed randomly, so that the accuracy of the recommended object meeting the preference of the user can be improved, and the user experience is improved.
According to the embodiment of the present disclosure, the object to be recommended may be any information, resource, or the like existing in the form of electronic data, for example, a video, an article, a commodity, or the like. And the recommendation of the object to be recommended is realized by transmitting the object to be recommended to each client through a network.
In some embodiments, the users in the user set are the users who receive the object to be recommended by using the respective clients. Where each user has a corresponding ID on the client through which the user can be obtained.
In some embodiments, the subset of related users for each user in the set of users may be a set of users with whom there is a direct connection. For example, the subset of related users of user A is a set of users having a friendship with user A. For another example, the related user subset of the user a is a set of users who focus on the user a.
In some embodiments, each user in the set of users performs a related operation on a recommended object indicating that they have a preference for the recommended object. For example, user A clicks on an article recommended to him, indicating that user A has a preference for that article.
It will be appreciated that the actions performed by the user on the recommended object may include a variety of actions, such as clicking, praise, sharing, commenting, favorites, or attention, among others. The user's preference for the recommended object varies with different operation instructions. For example, a praise indicates that the user's preference for the recommended object is greater than a click indicates that the user's preference for the recommended object is greater.
In some embodiments, a first subset of users corresponding to an object to be recommended is obtained according to historical user behavior of each user in a user set, where the historical user behavior is obtained when recommending a historical object to the user. The history object is an object recommended to the user before the object to be recommended.
In some embodiments, the object corresponds to a first category of a plurality of categories, each of the plurality of categories having a respective object, and as shown in fig. 3, obtaining, from the set of users, a first subset of users corresponding to the object to be recommended includes:
step S310: for each of the multiple classifications, obtaining a second user subset corresponding to the corresponding object of the classification, wherein each user in the second user subset has the first operation on the corresponding object of the classification;
step S320: for each of the plurality of categories, for each user in the second subset of users, recommending the corresponding object of the category to the relevant subset of users of the user to obtain a third subset of users corresponding to the user, wherein each user in the third subset of users has the first operation on the corresponding object;
step S330: for each of the plurality of classifications, determining a classified subset of users for the classification based on a third subset of users corresponding to each of the second subset of users corresponding to the respective object for that classification; and
step S340: determining the first subset of users based on the first classification and the classified subset of users for each of the plurality of classifications.
In the process of recommending an object to be recommended, a classification label of the object is often set first, and the object is recommended correspondingly based on the classification label of the object, wherein the classification label indicates the object classification of the object in a plurality of classifications.
In an embodiment according to the present disclosure, the accuracy of the obtained first subset of users is improved by obtaining a respective set of users for each of a plurality of classifications, and obtaining the first subset of users based on the corresponding classification of the object among the plurality of classifications, converting the process of obtaining the first subset of users for a single object into a process of obtaining a subset of users corresponding to all objects having the same classification as the object.
In some embodiments, there are a respective plurality of classifications for different objects to be recommended, where each classification corresponds to a plurality of objects.
For example, in the case where the object to be recommended is an article, the plurality of categories may include: finance, literature history, science, etc., each corresponding to a plurality of articles.
In some embodiments, the respective object of each of the plurality of classifications is any one of a plurality of objects of the classified object.
In some embodiments, the second subset of users corresponding to each of the plurality of categories is obtained by recommending a respective object for the category to each user in the set of users.
For example, a user would be added to a second subset of users corresponding to the category as the user would have had a first action on the corresponding object.
In some embodiments, for each of the plurality of categories, the users whose recommended acceptance rate in the second user subset corresponding to the respective object of the category is greater than a preset threshold are added to the classified user subset of the category to obtain the classified user subset of the category. And the recommendation acceptance rate indicates the ratio of the number of the users in the third user subset corresponding to the user to the number of the users in the related user subset of the user.
In some embodiments, for each of the plurality of categories, the categorized subset of users of the category includes a preset number of users in the second subset of users to which the category corresponds, wherein a ratio of a number of users in the third subset of users to which each user in the categorized subset of classified users corresponds to the number of users of the respective subset of users is greater than a ratio of a number of users in the third subset of users to which the second user in the second subset of users to which the respective object of the category corresponds to the respective subset of users to the number of users of the respective subset of users, wherein the category is distinct from each user in the third subset of users.
And determining users in the second user subset corresponding to each classification, which have a large ratio of the number of the users in the third user subset to the number of the corresponding user subsets, as the users in the classified user subsets, so that the determined corresponding user subsets are users with large influence, and the accuracy of the obtained first user subset corresponding to the object to be recommended is ensured.
In some embodiments, after performing steps S310-S320 for each of a plurality of classes by classifying a corresponding plurality of objects, respectively, a classified user subset of the class is obtained based on a second user subset corresponding to each of the plurality of objects and a third user subset corresponding to each of the users in the second user subset.
In some embodiments, as shown in fig. 4, determining whether to recommend the object to a target user of the set of users based on the first subset comprises:
step S410: based on the first subset, obtaining a predicted preference of the target user for the object; and
step S420: determining whether to recommend the object to the target user based on the predicted preferences.
The method comprises the steps of obtaining prediction preference of a target user on an object based on a first user subset, wherein the prediction preference indicates whether the target user has preference, and obtaining the prediction preference of the target user on the object based on the first user subset which comprises the preference of each user in the first user subset for the object to be recommended based on the first user subset.
In some embodiments, the predicted preference of the target user for the object is obtained by calculating a similarity of each user in the first subset to the target user. For example, if the number of users in the first subset whose similarity to the target user is greater than the preset value exceeds a preset number threshold, it is determined that the target user has a preference for the object.
In some embodiments, as shown in fig. 5, obtaining the predicted preference of the target user for the object based on the first subset comprises:
step S510: obtaining a vector representation for each user in the first subset and a vector representation for the target user;
step S520: obtaining a vector representation of the object based on the vector representation of each user in the first subset; and
step S530: obtaining the prediction preference based on the vector representation of the object and the vector representation of the target user.
And obtaining a vector representation of the object based on the vector of the first user subset, wherein the first user subset is obtained based on the first user subset corresponding to the object to be recommended and contains the preference of each user in the first user subset for the object, so that the obtained vector representation of the object comprises the preference of the first user subset, and the object is more accurately characterized.
In some embodiments, the representation is performed by obtaining each user in the first subset and the target user employment as a vector. For example, each user is represented as a vector
。
In some embodiments, as shown in fig. 6, obtaining the vector representation of the object based on the vector representation of each user in the first subset comprises:
step S610: clustering the first subset based on the vector representation of each user in the first subset to obtain a plurality of cluster centers; and
step S620: based on the plurality of cluster centers, a vector representation of the object is obtained.
By clustering the plurality of users in the first subset, a vector representation of the object is obtained, such that the obtained vector representation of the object is accurate.
In some embodiments, a Kmeans clustering is employed to obtain cluster centers for a plurality of vector representations of a plurality of users in the first subset; a vector representation of the object is obtained by using linear weighting on the cluster centers.
For example, a vector representation of an object is obtained by equation (1)
：
Wherein the content of the first and second substances,
represents the i-th cluster center vector,
and the preset weight parameter represents the ith clustering center vector, wherein i is a positive integer smaller than k, and k is the number of the clustering center vectors.
In some embodiments, the first subset includes a second subset, wherein each user in the second subset has a second operation on the object, the second operation indicating a preference for the object, wherein the second operation indicates a greater degree of preference for the object than the first operation. Clustering the first subset and the second subset respectively to obtain a plurality of clustering centers of the first subset and a plurality of clustering centers of the second subset; a vector representation of the object is obtained based on the plurality of cluster centers of the first subset and the plurality of cluster centers of the second subset, respectively.
For example, the vector of objects obtained based on the plurality of cluster centers of the first subset is represented as
(ii) a The vector representation of the object obtained based on the plurality of cluster centers of the second subset is
。
In some embodiments, the first operation comprises a click and the second operation comprises: like, share, comment, collect, or follow.
In some embodiments, as shown in fig. 7, obtaining the prediction preference based on the vector representation of the object and the vector representation of the target user comprises:
step S710: obtaining an evaluation index corresponding to the object, wherein the evaluation index indicates the preference degree of the first subset to the object; and
step S720: determining the prediction preference based on the evaluation index, the vector representation of the object, and the vector representation of the target user.
And determining the prediction preference of the target user for the object by obtaining the evaluation index indicating the degree of the preference of the first subset for the object, so that the obtained prediction preference of the target user for the object is quantified according to the user behaviors of the users in the first subset, and further improving the accuracy of the obtained prediction preference.
In some embodiments, the evaluation index includes:
a point-to-area ratio indicating a ratio of a number of users of the first subset to a number of users of the first subset;
an average elapsed time period indicating a sum of a length of time each user in the first subset spent on the object; or
An interaction rate indicating a ratio of a number of users in a second subset of the first subset to a number of users of the first subset, wherein each user in the second subset has a second operation on the object, the second operation indicating a preference for the object, wherein the second operation indicates a greater degree of preference for the object than the first operation.
For example, the number of users in the first subset is M, the number of users in the first subset is N, and the number of users in the second subset is K, where K, M, and N are positive integers; the dot-to-area ratio is obtained by the formula (2)
：
Obtaining the average spent time by equation (3)
：
Wherein the content of the first and second substances,
the sum of the lengths of time spent on the object for each user in the first subset.
Obtaining the interaction rate by the formula (4)
：
In some embodiments, determining the prediction preference based on the evaluation index, the vector representation of the object, and the vector representation of the target user comprises:
obtaining a similarity between the vector representation of the object and the vector representation of the target user; and
and obtaining the prediction preference based on the similarity and the evaluation index.
In some embodiments, the similarity between the vector representation of the object and the vector representation of the target user is obtained by calculating a cosine similarity, wherein the vector representation of the object obtained for the plurality of cluster centers based on the first subset is obtained for the first subset
The first similarity is obtained by the formula (5)
：
Wherein the content of the first and second substances,
is a vector representation of the target user.
Vector representation of objects obtained for a plurality of cluster centers based on a second subset
The second similarity is obtained by the formula (6)
：
Wherein the content of the first and second substances,
is a vector representation of the target user.
In some embodiments, the prediction preference is obtained by a first similarity obtained for a vector representation of the object obtained based on the plurality of cluster centers of the first subset and a second similarity obtained for a vector representation of the object obtained based on the plurality of cluster centers of the first subset. For example, the prediction preference is obtained by formula (7)
：
Wherein the content of the first and second substances,
、
、
、
and
obtained by using the aforementioned formulas (2) to (6).
In some embodiments, the object is determined to be recommended to the target user in response to the predicted preference being greater than a preset value.
In some embodiments, the prediction preferences are added to the recommendation model for the target user such that the recommendation model makes a comprehensive recommendation based on the prediction preferences and other metrics.
According to another aspect of the present disclosure, there is also provided an object recommending apparatus, as shown in fig. 8, the apparatus 800 including: a first user set obtaining unit 810 configured to obtain, from a user set, a first user subset corresponding to an object to be recommended, where an influence of each user in the first user subset is greater than an influence of a first user in the user set different from each user in the first user subset, where each user in the user set has a corresponding user subset, and the influence indicates a possibility that each user in a relevant user subset of the corresponding user has a preference for an object preferred by the corresponding user; a first recommending unit 820 configured to recommend the object to each user in a first subset of users to obtain a first subset of the first set of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and a second recommending unit 830 configured to determine whether to recommend the object to a target user in the set of users based on the first subset, the target user being different from each user in the first subset of users.
In some embodiments, the object corresponds to a first category of a plurality of categories, each of the plurality of categories having a respective object, the first user set obtaining unit 810 includes: a first recommending subunit, configured to, for each of the multiple classifications, obtain a second subset of users corresponding to the corresponding object of the classification, where each user in the second subset of users has the first operation on the corresponding object of the classification; a second recommending subunit, configured to, for each of the multiple categories, recommend, for each user in the second subset of users, a corresponding object of the category to a relevant subset of users of the user to obtain a third subset of users corresponding to the user, where each user in the third subset of users has the first operation on the corresponding object; a second user set obtaining unit configured to determine a classified user subset of the classification based on a third user subset corresponding to each user in the second user subset corresponding to the classified corresponding object; and a determining unit configured to determine the first subset of users based on the first classification and the subset of classified users for each of the plurality of classifications.
In some embodiments, for each of the plurality of classifications, a ratio of a number of users in the third subset of users to a number of users in the respective subset of users for each user in the subset of classified users is greater than a ratio of a number of users in the third subset of users to a number of users in the respective subset of users in the second subset of users to which the respective object of the classification corresponds, wherein the distinction is made from each user in the third subset of users.
In some embodiments, the second recommendation unit 830 includes: a preference obtaining unit configured to obtain a predicted preference of the target user for the object based on the first subset; and a third recommending subunit configured to determine whether to recommend the object to the target user based on the prediction preference.
In some embodiments, the preference obtaining unit includes: a vector representation obtaining unit configured to obtain a vector representation of each user in the first subset and a vector representation of the target user; a characterization unit configured to obtain a vector representation of the object based on the vector representation of each user in the first subset; and a preference obtaining subunit configured to obtain the prediction preference based on the vector representation of the object and the vector representation of the target user.
In some embodiments, the vector representation obtaining unit comprises: a clustering unit configured to cluster the first subset based on the vector representation of each user in the first subset to obtain a plurality of cluster centers; and a characterization subunit configured for obtaining a vector representation of the object based on the plurality of cluster centers.
In some embodiments, the preference obtaining subunit includes: an evaluation index obtaining unit configured to obtain an evaluation index corresponding to the object, the evaluation index indicating a degree of preference of the first subset for the object; and a determining subunit configured to determine the prediction preference based on the evaluation index, the vector representation of the object, and the vector representation of the target user.
In some embodiments, the evaluation index includes: a point-to-area ratio indicating a ratio of a number of users of the first subset to a number of users of the first subset; an average elapsed time period indicating a sum of a length of time each user in the first subset spent on the object; or an interaction rate indicating a ratio of a number of users in a second subset of the first subset to a number of users of the first subset, wherein each user in the second subset has a second operation on the object, the second operation indicating a preference for the object, wherein the second operation indicates a greater degree of preference for the object than the first operation.
In some embodiments, the first operation comprises a click and the second operation comprises: like, share, comment, collect, or follow.
According to another aspect of the present disclosure, an electronic device includes: 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 a method according to embodiments of the present disclosure.
According to another aspect of the present disclosure, a non-transitory computer-readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to the embodiments of the present disclosure.
According to another aspect of the disclosure, a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method according to embodiments of the disclosure.
In the technical scheme of the disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the common customs of public order.
According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.
Referring to fig. 9, a block diagram of a structure of an electronic device 900, which 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 device is 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the electronic apparatus 900 includes a computing unit 901, which can perform various appropriate actions and processes in accordance with 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 calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to 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, and the input unit 906 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may 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 control. Output unit 907 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 908 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices via 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 (TM) 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 the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 901 performs the various methods and processes described above, such as the method 200. For example, in some embodiments, the method 200 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 loaded into RAM 903 and executed by computing unit 901, may perform one or more of the steps of method 200 described above. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the method 200 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 circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods 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, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 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 a pointing device (e.g., a mouse or a trackball) by which a user may 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), and the Internet.
The computer system may include clients and servers. A client and server are generally 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 combining a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical aspects of the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described 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 as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.
Claims (18)
1. An object recommendation method comprising:
obtaining a first user subset corresponding to an object to be recommended from a user set, wherein the influence of each user in the first user subset is greater than the influence of a first user in the user set different from each user in the first user subset, wherein each user in the user set has an associated user subset, and the influence indicates the possibility that each user in the associated user subset of the corresponding user has a preference for the object preferred by the corresponding user;
recommending the object to each user in a first subset of users to obtain a first subset of the first subset of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and
determining, based on the first subset, whether to recommend the object to a target user of the set of users, the target user being distinct from each user of the first subset of users, wherein the determining, based on the first subset, whether to recommend the object to the target user of the set of users comprises:
based on the first subset, obtaining a predicted preference of the target user for the object; and
determining whether to recommend the object to the target user based on the predicted preferences.
2. The method of claim 1, wherein the object corresponds to a first category of a plurality of categories, each category of the plurality of categories having a respective object, the obtaining, from a set of users, a first subset of users corresponding to the object to be recommended comprising:
for each of the plurality of classifications,
obtaining a second user subset corresponding to the classified corresponding object, wherein each user in the second user subset has the first operation on the classified corresponding object;
for each user in the second user subset, recommending the classified corresponding object to the relevant user subset of the user to obtain a third user subset corresponding to the user, wherein each user in the third user subset has the first operation on the corresponding object;
determining a classified user subset of the classification based on a third user subset corresponding to each user in the second user subset corresponding to the classified corresponding object; and
determining the first subset of users based on the first classification and the classified subset of users for each of the plurality of classifications.
3. The method of claim 2, wherein, for each of the plurality of classifications, a ratio of a number of users in the third subset of users to a number of users in the subset of related users for each user in the subset of classified users is greater than a ratio of a number of users in the third subset of users to a number of users in the subset of related users for a second user in the second subset of users to which the respective object of the classification corresponds, wherein the second user is distinct from each user in the subset of classified users.
4. The method of claim 1, wherein the obtaining the predicted preference of the target user for the object based on the first subset comprises:
obtaining a vector representation for each user in the first subset and a vector representation for the target user;
obtaining a vector representation of the object based on the vector representation of each user in the first subset; and
obtaining the prediction preference based on the vector representation of the object and the vector representation of the target user.
5. The method of claim 4, wherein the obtaining a vector representation of the object based on the vector representation of each user in the first subset comprises:
clustering the first subset based on the vector representation of each user in the first subset to obtain a plurality of cluster centers; and
based on the plurality of cluster centers, a vector representation of the object is obtained.
6. The method of claim 4, wherein the obtaining the prediction preference based on the vector representation of the object and the vector representation of the target user comprises:
obtaining an evaluation index corresponding to the object, wherein the evaluation index indicates the preference degree of the first subset for the object; and
determining the prediction preference based on the evaluation index, the vector representation of the object, and the vector representation of the target user.
7. The method of claim 6, wherein the evaluation index comprises:
a point-to-area ratio indicating a ratio of a number of users of the first subset to a number of users of the first subset;
an average elapsed time period indicating a sum of a length of time each user in the first subset spent on the object; or alternatively
An interaction rate indicating a ratio of a number of users in a second subset of the first subset to a number of users of the first subset, wherein each user in the second subset has a second operation on the object, the second operation indicating a preference for the object, wherein the second operation indicates a greater degree of preference for the object than the first operation.
8. The method of claim 7, wherein the first operation comprises a click and the second operation comprises: like, share, comment, collect, or follow.
9. An object recommendation device comprising:
a first user subset obtaining unit, configured to obtain, from a user set, a first user subset corresponding to an object to be recommended, where an influence of each user in the first user subset is greater than an influence of a first user in the user set different from each user in the first user subset, where each user in the user set has a related user subset, and the influence indicates a possibility that each user in the related user subset of the corresponding user has a preference for the object preferred by the corresponding user;
a first recommending unit configured to recommend the object to each user in a first subset of users to obtain a first subset of the first subset of users, each user in the first subset having a first operation on the object, the first operation indicating a preference for the object; and
a second recommending unit configured to determine whether to recommend the object to a target user in the user set based on the first subset, the target user being different from each user in the first subset of users, wherein the second recommending unit includes:
a preference obtaining unit configured to obtain a predicted preference of the target user for the object based on the first subset; and
a third recommending subunit configured to determine whether to recommend the object to the target user based on the prediction preference.
10. The apparatus of claim 9, wherein the object corresponds to a first category of a plurality of categories, each of the plurality of categories having a respective object, the first subset of users acquisition unit comprising:
a first recommending subunit, configured to, for each of the multiple classifications, obtain a second subset of users corresponding to the corresponding object of the classification, where each user in the second subset of users has the first operation on the corresponding object of the classification;
a second recommending subunit, configured to, for each of the plurality of categories, recommend, for each user in the second subset of users, a corresponding object of the category to a relevant subset of users of the user to obtain a third subset of users corresponding to the user, where each user in the third subset of users has the first operation on the corresponding object;
a second user set obtaining unit configured to determine a classified user subset of the classification based on a third user subset corresponding to each user in the second user subset corresponding to the classified corresponding object; and
a determining unit configured to determine the first subset of users based on the first classification and the subset of classified users for each of the plurality of classifications.
11. The apparatus of claim 10, wherein, for each of the plurality of classifications, a ratio of a number of users in the third subset of users to a number of users in the subset of related users for each user in the subset of classified users is greater than a ratio of a number of users in the third subset of users to a number of users in the subset of related users for a second user in the second subset of users to which the respective object of the classification corresponds, wherein the second user is distinct from each user in the subset of classified users.
12. The apparatus of claim 9, wherein the preference obtaining unit comprises:
a vector representation obtaining unit configured to obtain a vector representation of each user in the first subset and a vector representation of the target user;
a characterization unit configured to obtain a vector representation of the object based on the vector representation of each user in the first subset; and
a preference obtaining subunit configured to obtain the prediction preference based on the vector representation of the object and the vector representation of the target user.
13. The apparatus of claim 12, wherein the vector representation obtaining unit comprises:
a clustering unit configured to cluster the first subset based on the vector representation of each user in the first subset to obtain a plurality of cluster centers; and
a characterization subunit configured for obtaining a vector representation of the object based on the plurality of cluster centers.
14. The apparatus of claim 12, wherein the preference acquisition subunit comprises:
an evaluation index obtaining unit configured to obtain an evaluation index corresponding to the object, the evaluation index indicating a degree of preference of the first subset for the object; and
a determining subunit configured to determine the prediction preference based on the evaluation index, the vector representation of the object, and the vector representation of the target user.
15. The apparatus of claim 14, wherein the evaluation index comprises:
a point-to-area ratio indicating a ratio of a number of users of the first subset to a number of users of the first subset;
an average elapsed time period indicating a sum of a length of time each user in the first subset spent on the object; or
An interaction rate indicating a ratio of a number of users in a second subset of the first subset to a number of users of the first subset, wherein each user in the second subset has a second operation on the object, the second operation indicating a preference for the object, wherein the second operation indicates a greater degree of preference for the object than the first operation.
16. The apparatus of claim 15, wherein the first operation comprises a click and the second operation comprises: like, share, comment, collect, or follow.
17. 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 of any one of claims 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
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