Information recommendation method, device, electronic device and storage medium

Abstract

The present disclosure provides an information recommendation method in the field of artificial intelligence technology, particularly in the field of big data, intelligent recommendation, and intelligent e-commerce technology. The specific implementation scheme is: based on the configuration information of the target entity, determining the entity features and pattern features of the corresponding activity pattern for the target entity, the configuration information characterizing at least one activity pattern of the target entity with respect to the object; based on the interest of multiple objects with respect to the target entity, determining a target object set corresponding to the target entity, the target object set including at least one target object, the target object's interest with respect to the target entity being greater than an interest threshold; pushing the target entity and the corresponding activity pattern to at least one target object in the target object set. The present disclosure also provides an information recommendation device, an electronic device, and a storage medium.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence technology, particularly big data, intelligent recommendation, and intelligent e-commerce technology, and can be used in scenarios such as product recommendation. More specifically, the present disclosure provides an information recommendation method, device, electronic device, and storage medium. Background Art

[0002] With the development of mobile Internet technology, more and more consumers can use different applications to shop, watch videos, etc. Currently, in these applications, products and videos of interest can be recommended to users, but the accuracy of the recommendations is low. Summary of the Invention

[0003] The present disclosure provides an information recommendation method, apparatus, device, and storage medium.

[0004] According to one aspect of the present disclosure, an information recommendation method is provided, the method comprising: determining entity features and pattern features of corresponding activity patterns for a target entity based on configuration information of the target entity, the configuration information characterizing at least one activity pattern of the target entity with respect to an object; determining a target object set corresponding to the target entity based on the interest of a plurality of objects with respect to the target entity, the target object set comprising at least one target object, the interest of the target object with respect to the target entity being greater than an interest threshold; and pushing the target entity and the corresponding activity pattern to at least one target object in the target object set.

[0005] According to another aspect of the present disclosure, an information recommendation device is provided, which includes: a first determination module for determining entity features and pattern features of corresponding activity patterns for a target entity based on configuration information of the target entity, the configuration information representing at least one activity pattern of the target entity with respect to an object; a second determination module for determining a target object set corresponding to the target entity based on the interest of multiple objects with respect to the target entity, the target object set including at least one target object, the interest of the target object with respect to the target entity being greater than an interest threshold; and a push module for pushing the target entity and the corresponding activity pattern to at least one target object in the target object set.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided according to the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method provided according to the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method provided according to the present disclosure when executed by a processor.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0011] Figure 1 The system architecture of the information recommendation method, apparatus, device, medium, and program product according to the embodiments of the present disclosure is schematically shown;

[0012] Figure 2 The following schematically shows a flow chart of an information recommendation method according to an embodiment of the present disclosure;

[0013] Figure 3 The information recommendation process according to one embodiment of the present disclosure is schematically shown;

[0014] Figure 4 The following schematically illustrates a process of determining an object set according to an embodiment of the present disclosure;

[0015] Figure 5 The following schematically shows an application diagram of the information recommendation method according to an embodiment of the present disclosure;

[0016] Figure 6 Schematically shows a schematic diagram of a first billboard according to an embodiment of the present disclosure;

[0017] Figure 7 Schematically shows a schematic diagram of a second signboard according to an embodiment of the present disclosure;

[0018] Figure 8 is a block diagram of an information recommendation device according to an embodiment of the present disclosure; and

[0019] Figure 9 is a block diagram of an electronic device to which an information recommendation method can be applied according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0022] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0023] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0024] With the development of mobile Internet technology, more and more consumers can use different applications to shop, watch videos, etc. Currently, in these applications, products and videos of interest can be recommended to users, but the accuracy of the recommendations is low.

[0025] For example, in the e-commerce business, many businesses sell their products online by joining e-commerce platforms, allowing consumers to purchase their products through these platforms. However, many small and micro businesses lack the experience and resources to operate e-commerce businesses, making it difficult to meet consumers' diverse product demands. This results in poor product recommendations and, as a result, faces intense competition in the industry.

[0026] To alleviate the competitive pressure on merchants, e-commerce platforms usually provide support projects to merchants on the platform. Currently, the support methods for merchants can include one of the following:

[0027] (1) Commodity cards are commission-free, reducing merchants’ operating costs;

[0028] (2) Free entry into the e-commerce platform to solve the problem of difficulty in opening stores for small and medium-sized and individual businesses and alleviate the financial pressure on businesses;

[0029] (3) Merchant-specific identity identification helps merchants gain better traffic exposure.

[0030] However, simply reducing merchant costs and alleviating financial pressures won't solve their fundamental problems. Simply increasing merchant exposure not only fails to effectively help merchants acquire effective users, but also easily degrades the user experience. Therefore, accurately selecting target user groups and recommending products of interest to them has become crucial for merchant operations strategies.

[0031] To this end, embodiments of the present disclosure propose an information recommendation solution. For example, based on configuration information of a target entity, entity features and corresponding activity patterns are determined for the target entity, where the configuration information represents at least one activity pattern of the target entity with respect to an object; based on the interest levels of multiple objects with respect to the target entity, a target object set corresponding to the target entity is determined, where the target object set includes at least one target object whose interest level with respect to the target entity is greater than an interest level threshold; and the target entity and corresponding activity pattern are pushed to at least one target object in the target object set.

[0032] According to an embodiment of the present disclosure, entity features and pattern features used to filter target objects are determined through the configuration information of the target entity. Target objects are filtered out based on the interest of different objects in the target entity corresponding to the entity features and pattern features. And relevant information of the target entity is accurately recommended to the target object. Not only can objects that match the entity be mined more accurately; it can also quickly establish a connection between the target entity and the target object, making the connection between the target object and the target entity closer. This makes the recommendation results of the target entity more in line with user needs, helping to improve operational efficiency and user experience.

[0033] It should be noted that the information recommendation method proposed in the present disclosure can be applied to scenarios such as e-commerce live broadcast recommendations, intelligent marketing, and value analysis of homogeneous groups, so as to determine the target users of the business, achieve precision marketing, improve the accuracy of recommendation strategies, and increase the utilization rate of traffic. The information recommendation method provided in the embodiments of the present disclosure does not limit the application scenarios.

[0034] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0035] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0036] Figure 1The system architecture of the information recommendation method, apparatus, device, medium and program product according to the embodiment of the present disclosure is schematically shown. It should be noted that Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0037] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, a server 105, and a database 106. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may also be used to provide a medium for communication links between the server 105 and the database 106. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0038] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0039] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0040] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as configuration information, and feedback the processing results (such as obtaining or generating a target object based on the configuration information of the target entity and pushing the target entity to the target object) to the terminal device.

[0041] The server can be a cloud server, also known as a cloud computing server or cloud host. It is a hosting product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or "VPS"). The server can also be a distributed system server or a server integrated with blockchain.

[0042] The system architecture 100 may also include one or more databases 106. In some embodiments, these databases can be used to store data and other information. For example, one or more of the databases 106 can be used to store data such as user consumption behavior data, user portraits, user portrait tags, etc. The data repository 106 can reside in various locations. For example, the data repository used by the server 105 can be local to the server 105, or can be far away from the server 105 and can communicate with the server 105 via a network-based or dedicated connection. The data repository 106 can be of different types. In certain embodiments, the data repository used by the server 105 can be a database, such as a relational database. One or more of these databases can store, update, and retrieve data to and from the database in response to commands.

[0043] In some embodiments, one or more of the databases 106 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.

[0044] It should be noted that the information recommendation method provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the information recommendation apparatus provided in the embodiments of the present disclosure can generally be set in the server 105. The information recommendation method provided in the embodiments of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the information recommendation apparatus provided in the embodiments of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0045] Alternatively, the information recommendation method provided in the embodiments of the present disclosure may also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may also be executed by another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the information recommendation apparatus provided in the embodiments of the present disclosure may also be provided in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may be provided in another terminal device different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0046] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0047] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0048] The above describes the system architecture of the information recommendation method provided by the present disclosure. Figure 2 For example, the information recommendation process of the present disclosure is further explained.

[0049] Figure 2 The flowchart of the information recommendation method according to the embodiment of the present disclosure is schematically shown.

[0050] like Figure 2 As shown, the method 200 may include operations S210 to S230.

[0051] In operation S210 , entity features for the target entity and pattern features corresponding to the activity patterns are determined based on configuration information of the target entity, the configuration information representing at least one activity pattern of the target entity with respect to the object.

[0052] In the embodiments of the present disclosure, the target entity can be any type of entity being recommended. For example, the target entity can be a merchant, a product, a host, advertising information, etc. The embodiments of the present disclosure do not limit this.

[0053] Configuration information can be used to characterize the target entity's recommendation strategy. For example, configuration information may include the target entity's recommendation method and scope. For example, configuration information may include an e-commerce platform providing financial support to Merchant A (the target entity), coupons to users who purchase Merchant A's products, and free shipping for users who purchase Merchant A's products. Configuration information may also include traffic support provided by Merchant A (the target entity) for brand promotion, such as recommending Merchant A on the homepages of certain applications through relevant platforms to generate traffic exposure.

[0054] It should be noted that the configuration information can be information provided by the merchant to the recommendation platform, or it can be information provided by the recommendation platform to the merchant. A configuration information can include one or more activity modes. This disclosure does not limit the number of activity modes.

[0055] Entity features can be descriptive information about the target entity. For example, if merchant A sells stationery products, the entity features of merchant A may include: stationery, A (the brand name of merchant A).

[0056] The activity pattern may be information corresponding to the recommended method (activity pattern) in the configuration information of the target entity. The pattern feature may be information describing the activity pattern. For example, if the configuration information provides a coupon for users who purchase products from merchant A, the pattern feature may be the coupon.

[0057] For example, the configuration information for down jacket merchant A may be: the e-commerce platform provides coupons to users who purchase products of merchant A. The entity features of merchant A may include: down jacket and A, and the pattern features may include: coupons.

[0058] In operation S220 , a target object set corresponding to the target entity is determined based on the interest of the plurality of objects with respect to the target entity. The target object set includes at least one target object whose interest with respect to the target entity is greater than an interest threshold.

[0059] In the disclosed embodiments, interest level can be the degree of interest of an object in a target entity. Interest level can be reflected through entity features and pattern features. For example, user 1 may have a first level of interest in down jackets; user 1 may have a second level of interest in A (brand name); and user 1 may have a third level of interest in down jackets with coupons. The degree of interest is reflected differently for different entities. For example, when the target entity is a product, interest level can indicate the user's willingness to purchase the product. When the target entity is a live streamer, interest level can indicate whether the user is willing to follow the live streamer.

[0060] The interest threshold can be set based on the characteristics of the model. For example, the e-commerce platform may issue coupons to users whose interest in merchant A's products exceeds 50%. The e-commerce platform may also issue coupons to users whose interest in merchant A's products exceeds 30%. This disclosure does not limit the value of the interest threshold.

[0061] The target object set is a set consisting of one or more target objects. All target objects in the target object set have an interest in the target entity, and the interest of the target objects in the target entity is greater than an interest threshold.

[0062] The target object set can be understood as a set of users whose interest in the target entity, different activity modes, or target entities with different activity modes is greater than 30%. For example, users 1-10 are more than 30% interested in purchasing stationery products with coupons. Users 11-20 are more than 30% interested in purchasing stationery products with free shipping. Users 21-30 are more than 30% interested in stationery merchant A. Then users 1-30 together constitute the target object set. For target entities, different activity modes, or target entities with different activity modes with an interest level greater than 30%, there may be duplicate users, in which case the duplicate users can be merged.

[0063] It should be noted that, in the process of determining the target object screening, the more entity features and pattern features are introduced, the higher the degree of adaptation between the determined target object and the target entity.

[0064] For example, if the configuration information of down jacket merchant A indicates that the e-commerce platform will issue coupons to users whose interest in merchant A's products is greater than 10%, users can be screened based on the combination of all entity features: down jacket, A, and all pattern features: coupon, and interest threshold greater than 10%, to obtain users whose interest in brand A down jackets with coupons is greater than 10%.

[0065] In operation S230 , the target entity and the corresponding activity pattern are pushed to at least one target object in the target object set.

[0066] In an embodiment of the present disclosure, after determining the target object set, information about target entities with different activity modes can be pushed to the corresponding target objects. That is, the target objects determined by the combination of different entity features and pattern features are respectively. For example, user 1-10 has a greater than 30% interest in purchasing stationery products with coupons. Then the information of stationery merchant A with purchase coupons is pushed to user 1-10. User 11-20 has a greater than 30% interest in purchasing stationery products with free shipping. Then the information of stationery merchant A with purchase free shipping is pushed to user 11-20. User 21-30 has a greater than 30% interest in stationery merchant A. Then the information of stationery merchant A is pushed to user 21-30. That is, users who have a certain degree of interest in different activity modes of merchant A are pushed the information of merchant A and / or activity mode information respectively. The information of merchant A can be the product information of merchant A or the store information of merchant A.

[0067] Through the disclosed embodiments, the entity features and pattern features used to filter target objects are determined through the configuration information of the target entity. Target objects are filtered out based on the interest of different objects in the target entity corresponding to the entity features and pattern features. And relevant information of the target entity is accurately recommended to the target object. Not only can objects that match the entity be mined more accurately; it can also quickly establish a connection between the target entity and the target object, making the connection between the target object and the target entity closer. This makes the recommendation results of the target entity more in line with user needs, helping to improve operational efficiency and user experience.

[0068] According to the interest of multiple objects in the target entity, a target object set corresponding to the target entity is determined, including: obtaining at least one target feature, the target feature including entity features and pattern features; according to the at least one target feature, determining a candidate object set associated with the at least one target feature from multiple object sets, the candidate object set including at least one candidate object; when the interest of the candidate object in the target entity is greater than an interest threshold, determining the candidate object as the target object, obtaining the target object set, and determining the information recommendation result.

[0069] The target feature may be determined based on the target entity and configuration information of the target entity, and may include one or more entity features of the target entity, one or more pattern features, or at least one entity feature and at least one pattern feature.

[0070] As an example, the target feature may also be determined based on the target entity and configuration information of the target entity.

[0071] The object set can be determined by performing association analysis on different objects and different entities.

[0072] The candidate object set may include one or more candidate objects, each of which has interest for the target entity. The candidate object set includes candidate objects whose interest is greater than or equal to an interest threshold and whose interest is less than the interest threshold.

[0073] In one example, stationery merchant A's configuration information provides coupons to users whose interest in merchant A's products exceeds 10%. Users can be filtered based on the entity characteristics: stationery, A (brand name), and coupon, resulting in users 1-50 interested in brand A stationery with coupons. Among users 1-50, users 1-30 have an interest of 10% or greater in brand A stationery with coupons, while users 31-50 have an interest of less than 10%. Based on the interest threshold of 10%, users 1-50 are filtered again, ultimately selecting users 1-30, and pushing merchant A's products and coupons to them.

[0074] It can be understood that by screening objects based on entity features and pattern features corresponding to the target entity to obtain candidate objects, these candidate objects are further screened based on interest levels to obtain the target object. Therefore, setting target features and interest level thresholds to continuously screen objects not only improves the accuracy of identifying target objects, enabling more precise targeting of target entity marketing activities and improving operational efficiency, but also meets users' diverse needs for the same entity, enhancing the user experience.

[0075] In the embodiment of the present disclosure, information recommendation can be determined based on the target characteristics and interest of the target entity. Figure 3 Take as an example to illustrate the process of determining information recommendation.

[0076] Figure 3 The information recommendation process according to one embodiment of the present disclosure is schematically illustrated.

[0077] like Figure 3 As shown, configuration information 301 of a target entity is obtained, and entity features 302 and pattern features 303 of corresponding activity patterns for the target entity are determined based on the configuration information of the target entity. At least one target feature is obtained, and screening conditions for candidate objects are constructed. The target features include entity features 302 and pattern features 303. Based on the at least one target feature, object matching is performed from a plurality of pre-determined object sets 304, and a candidate object set 305 associated with the at least one target feature is screened. Candidate object set 305 includes at least one candidate object. If the candidate object's interest in the target entity is greater than an interest threshold, the candidate object is determined to be a target object, a target object set 306 is obtained, and information recommendation results 307 are determined.

[0078] According to an embodiment of the present disclosure, multiple object sets are determined by the following operations: obtaining first data of multiple objects and second data of multiple entities, the first data representing the descriptive information and behavioral information of the objects, and the second data representing the descriptive information of the entities; generating at least one first label for each object based on the first data, the first label including at least one of the descriptive features and preference features of the object; generating at least one second label for each entity based on the second data, the second label including the entity features and pattern features of the entity; determining multiple object sets based on the first label and the second label, the object set representing the interest of the object with the first label in the entity with the second label.

[0079] The first data may be the object's descriptive information and behavioral information. The descriptive information may be basic information about the object, such as the user's age, region, gender, occupation, life stage, and income level. The behavioral information may be information about the object's access, such as the web content the user browses, product purchases, and videos watched. Based on the descriptive information, descriptive features of the object can be generated. Based on the behavioral information, preference features of the object can be generated.

[0080] Users are aware of and agree to the acquisition and use of First Data, which complies with relevant laws and regulations and does not violate public order and good morals.

[0081] The first tag can be used to characterize features that match the object's descriptive features and preference features. The first tag can be used to quickly identify target objects that match the target features among multiple objects. For example, the first tag can be male, aged 30-40, married, likes car models, and likes to use coupons to purchase products.

[0082] The second data may be descriptive information of the entity. The descriptive information may be basic information of the entity, such as the type and selling points of the product, the name of the merchant, the products sold, and honors.

[0083] The second tag can be used to represent features that match the entity's physical features and pattern features. The second tag can be used to quickly obtain the entity's features. For example, the second tag could be Brand A, Stationery, Coupon for product purchase, Free shipping for product purchase, etc.

[0084] An object set can be a collection of objects with a first tag and entities with a second tag. The objects in the object set have varying degrees of interest in the entities with the second tag. For example, users interested in coupons for stationery purchases include User 1, User 2, User 3, and User 4. Users interested in free shipping on stationery purchases include User 1, User 2, and User 5.

[0085] In some embodiments, the first tag of an object can be updated periodically (for example, once a month), and the set of objects corresponding to the first and second tags can be updated simultaneously. Correlated features can be mined based on the first and second tags of objects at different times, thereby making information recommendations more accurate.

[0086] For example, the descriptions and behavior information of multiple users, as well as the descriptions and activity patterns of multiple products, are analyzed to determine each user's interest in different products, as well as each user's interest in different activity patterns for the same product. This yields objects corresponding to different products combined with different activity patterns. An object set can be a collection of objects with a first label and a second label. For example, object set 1: Users interested in purchasing stationery with coupons include user 1, user 2, user 3, and user 4. User 1, user 2, user 3, and user 4 share the following characteristics: female, aged 35-40, with children. Therefore, the first label is: female, aged 35-40, with children. The second label is: interested in purchasing stationery with coupons.

[0087] The target entity is stationery merchant A, and the configuration information is: coupons are issued to users whose interest in stationery products is greater than 10%. During the information recommendation process, the target entity and configuration information can be used to determine the target feature: stationery merchants that issue coupons. Based on multiple object sets (with a first label and a second label), matching is performed based on the target feature and the second label. If the target feature and the second label match, object set 1 is obtained. Object set 1 is used as the candidate object set, and users 1 (50% interest), user 2 (60% interest), user 3 (60% interest), and user 4 (5% interest) are respectively selected as candidates. The candidate objects in object set 1 are filtered based on the interest threshold of 10%, resulting in the target object set (user 1 (50% interest), user 2 (60% interest), and user 3 (60% interest)).

[0088] It can be understood that determining the first label based on the object's descriptive information and behavioral information can more deeply and accurately reveal the object's level of interest in different entities. By using the first label to more accurately characterize different objects and the second label to more accurately characterize different entities, it can accurately discover objects that are interested in the same type of entity, thereby improving the applicability of the object set in different application scenarios.

[0089] According to an embodiment of the present disclosure, multiple object sets are determined based on a first label and a second label, including: performing a cross-analysis on the first label and the second label to determine the degree of association between the first label and the second label; when the degree of association is greater than a preset threshold, determining an association feature between the first label and the second label; and determining multiple object sets based on the association feature, the first label, and the second label.

[0090] For example, an association feature may be a mapping feature formed by an object with a first label and an entity with a second label. The degree of association may represent the degree of association between the object with the first label and the entity with the second label. The degree of association between the first label and the second label in the association feature is greater than a preset threshold. For the association feature, an object set corresponding to the association feature may be constructed.

[0091] For example, if the correlation between women aged 25-30 and stationery products is 10, and the correlation between women aged 35-40 with children and stationery products is 50, and the preset correlation threshold is 40, then women aged 35-40 with children and stationery products are identified as the correlation feature. For women aged 35-40 with children (first label) and stationery products (second label), women aged 35-40 with children can be included as objects in the corresponding object set.

[0092] It is understandable that in the process of building an object set, understanding the correlation between objects and entities can help to more accurately screen out objects of interest to the entity.

[0093] According to an embodiment of the present disclosure, each object set in the plurality of object sets includes at least one object associated with a same entity and the interest level of the at least one object in the same entity.

[0094] The interest level is obtained by inputting the associated feature, the first label, and the second label into an interest level determination model to obtain the interest level of at least one object with the first label for at least one entity with the second label.

[0095] The object set includes multiple objects, each of which has different levels of interest for the same entity. The object set also has a mapping relationship between a first label and a second label. Here, the mapping relationship between the first label and the second label and the level of interest can be added to the object set as additional information.

[0096] The interest of an object in different entities can be predicted by an interest determination model.

[0097] The associated features, the first data, the second data, the first label, and the second label may be simultaneously input into the interest determination model to generate the interest of different objects with the first label to the entity with the second label.

[0098] In one example, consider the following: Women aged 35-40 with children (first label) - Stationery products (second label). The users associated with the first label are User 1, User 2, User 3, and User 4. The associated feature "Women aged 35-40 with children - Stationery products" and the first and second data of Users 1-4 are input into the interest determination model. The interest level of each user in stationery products is determined. For example, User 1 (interest level 50%), User 2 (interest level 60%), User 3 (interest level 60%), and User 4 (interest level 5%).

[0099] According to the embodiments of the present disclosure, the efficiency of predicting the interest of an object in an entity can be improved by utilizing the interest determination model. At the same time, combined with the association features, the association between the object and the entity can be understood, thereby improving the accuracy of interest prediction.

[0100] In the embodiment of the present disclosure, the plurality of object sets 304 may be determined in advance based on different objects and different entities. Figure 4 Take as an example to illustrate the process of determining the object set.

[0101] Figure 4 The diagram schematically illustrates a process of determining an object set according to an embodiment of the present disclosure.

[0102] like Figure 4 As shown, first data 401 of multiple objects and second data 402 of multiple entities are obtained. Based on the first data 401, one or more first tags 403 are generated for each object. Based on the second data 402, one or more second tags 404 are generated for each entity. A cross-analysis is performed on the first tags 403 and the second tags 404. If the feature correlation between the first tag 403 and the second tag 404 exceeds a preset threshold, the features having the first tag 403 and the second tag 404 are determined to be associated features 405. The associated features 405, the first tags 401, and the second tags 402 are input into an interest determination model to generate the interest of the object with the first tag 403 in the entity with the second tag 402, thereby obtaining an object set 304 related to the first tag 403 and the second tag 404. The interest of multiple objects with the first tag 403 in the object set 304 in the object set 304 in the object set 304 is used as auxiliary information of the object set 304. Ultimately, an object set 304 is obtained, which shows the interest of different objects in different entities.

[0103] According to an embodiment of the present disclosure, at least one first label is generated for each object based on the first data, including at least one of the following: based on the first data, an attribute label and a first model label of the object are generated, the attribute label includes at least one descriptive feature of the object, and the first model label includes the model type and model parameters used to generate the attribute label; based on the first data, a statistical label and a second model label of the object are generated, the statistical label includes at least one statistical feature of the object, and the second model label includes the model type and model parameters used to generate the statistical label; based on the first data, a preference label and a third model label of the object are generated, the preference label includes the object's preference features for different entities, and the third model label includes the model type and model parameters used to generate the preference features.

[0104] In one example, an attribute label and a first model label of the object are generated according to the first data.

[0105] For example, attribute tags can be used to tag users based on the terminal device they use. Attribute tags can be attributes of the user's descriptive information and can be directly extracted from the user's descriptive information data. For example, user gender, age, region, and device information such as mobile phone brand and operating system can be used for target object screening and as input parameters for preference tags.

[0106] In the process of obtaining different attribute labels for a user, a first model can be used. The user's first data is input into the first model to generate the user's attribute labels. Simultaneously, the model parameters of the first model can be stored. When subsequently generating attribute labels based on the user data, the first model with the corresponding parameters can be used to improve label generation efficiency.

[0107] In another example, a statistical label and a second model label of the object are generated based on the first data.

[0108] For example, statistical tags can be combined with the user's primary data to generate statistical groupings, reflecting the characteristics of the user data. For example, user preferences for e-commerce product categories and user activity can be determined based on user access behavior. Statistical tags can be used for personalized recommendations in information flow and to promote the activation of new and existing users.

[0109] In the process of obtaining different statistical labels for a user, a second model can be used. The user's first data is input into the second model to generate the user's statistical labels. Simultaneously, the model parameters of the second model can be stored. When subsequently generating statistical labels based on the user data, the second model with the corresponding parameters can be used to improve label generation efficiency.

[0110] In yet another example, a preference label and a third model label of the object are generated based on the first data.

[0111] For example, predicted tags can be features that predict user behavior preferences and tendencies based on user primary data. These tags primarily utilize deep learning algorithms to calculate tags such as the user's consumption intentions, attention intentions, and category preferences. For example, if a user has recently browsed a lot of winter down jackets and has a medium to high income, their price range and brand preferences for down jackets can be predicted.

[0112] When generating predictive labels based on the user's primary data, statistical labels, and attribute labels, different third models can be used for different types of predictive labels. For example, machine learning models such as logistic regression, decision tree, and principal component analysis can be used. For example, a decision tree model can be used to predict labels for a user's product purchase intention, while a logistic regression model can be used to predict a user's price sensitivity.

[0113] In yet another example, an attribute label, a first model label, a statistical label, a second model label, a preference label, and a third model label of the object are generated based on the first data.

[0114] It should be noted that any type of attribute label, statistical label, and preference label can be selected according to actual application requirements. This disclosure embodiment does not limit this. When selecting different types of labels, the model label corresponding to the type of label must also be used as the generated result.

[0115] It's understandable that attribute tags ensure the information recommendation system delivers highly relevant recommendations based on the subject's specific characteristics; statistical tags analyze the subject's behavioral data to tailor recommendations to their actual needs; and predictive tags proactively identify potential user needs and make forward-looking recommendations. Therefore, the use of user attribute tags, statistical tags, and predictive tags enhances the intelligence, personalization, and precision of information recommendations, making them more tailored to user needs, improving the user experience, and driving platform business growth.

[0116] In the embodiments of the present disclosure, information recommendation can be made through analysis at different levels. Figure 5 Take as an example to illustrate the application process of information recommendation.

[0117] Figure 5 The following schematically illustrates an application diagram of the information recommendation method according to an embodiment of the present disclosure.

[0118] like Figure 5As shown. In the operating environment 501, the user generates access behaviors through different applications in the terminal to form the user's original information, and the first original information is stored in the server as log data. In the buried log 502, the log management system can analyze the log data in the server in the form of buried logs to obtain the user's original information. In the database 503, the original information can be stored in different types of databases according to the needs of different data applications. At the same time, the basic information of different entities can also be recorded in the database for storage. In the data warehouse 504, the original information of the user and the basic information of different entities obtained from the database are input into the data warehouse. The different data layers in the data warehouse clean, count, aggregate, etc. the original information and basic information to generate user data for analysis and decision-making: first data and entity data: second data. In the object portrait 505, according to the first data of different users, different analysis models are used to analyze and obtain multiple portrait labels for each user. In the entity configuration 506, the object set of the user about different entities is determined in advance based on the labels of multiple users. For details, please refer to the above Figure 4 According to the configuration information of the target entity, determine the target object associated with the target entity and push the target entity to the target object. Figure 3 In the dashboard 507 , the interaction between the user and the target entity can be analyzed to generate different dashboards. The dashboard can be used to display the target entity's recommendation results for the user in multiple dimensions.

[0119] In one example, if strict data integrity is required for embedded log data, a first database (relational database, such as MySQL) can be used. If the user data volume is large, a second database (distributed file system) can be used. A third database (time series database) can also be used to store user data that requires time series data.

[0120] In one example, a data warehouse may include a data application layer, a data dimension layer, a data service layer, an intermediate data layer, a data access layer, and a basic data layer.

[0121] The data dimension layer stores data on different dimensions of users or entities. For example, tables for live broadcast rooms and anchor information are available. The data access layer retrieves raw user data from various business systems, typically including logging, ordering, logistics, and comment systems. The basic data layer cleans and transforms data from the data access layer to remove redundant, erroneous, or useless data, ensuring data accuracy and integrity. The data foundation layer does not involve complex logic, model algorithms, or other data. The intermediate data layer aggregates data from the basic data layer. For example, by introducing risk control models and cleansing rules, deeper analysis, feature extraction, and data aggregation can be performed. The data service layer constructs data tables based on specific business needs and business themes. The data application layer extracts data from the data warehouse based on business needs, performs necessary logical operations and processing, and generates visual reports or analytical results.

[0122] In one example, for user tags, a first model may be used to analyze first data to obtain attribute tags, a second model may be used to analyze the first data to obtain statistical tags, and a third model may be used to analyze the first data to obtain preference tags.

[0123] In one example, the results of information recommendations can be visualized and analyzed. From basic user behavior to successful purchases, different scenarios have different conversion paths. To facilitate metric monitoring and business analysis, a merchant store details dashboard (first dashboard 601), a user distribution dashboard (second dashboard 602), and a dashboard showing the effectiveness of different activity modes (third dashboard) can be constructed.

[0124] In the embodiment of the present disclosure, the dashboard can be generated based on the result of information recommendation. Figure 6 Take the following example to illustrate the display results of the first dashboard.

[0125] Figure 6 A schematic diagram of a first signboard according to an embodiment of the present disclosure is schematically shown.

[0126] like Figure 6 As shown, the example of an e-commerce platform supporting e-commerce merchants is used for explanation. After a merchant's products are recommended to a user, the user may purchase the product. Therefore, a first dashboard 601 can be generated based on the information recommendation results. In the first dashboard 601, data analysis can be performed on the user's product purchase situation, so that merchants can view the corresponding configuration information to determine the effectiveness of information recommendations. For example, the first dashboard can display the new customer transaction amount, the number of new customer transactions, the new customer unit price, the new customer conversion rate, new customer conversion analysis, customer data trends, and product data trends.

[0127] According to an embodiment of the present disclosure, the configuration information is determined by using the following operations: obtaining operating parameters of the target entity; and determining the configuration information corresponding to the target entity according to the operating parameters.

[0128] Exemplarily, the work parameters can be different stages of the entity's work. For example, the work parameters can include: primary stage and non-primary stage. For example, it can be the merchant's first time on the e-commerce platform. It can also be the anchor's first live broadcast.

[0129] Different strategies can be used to determine an entity's configuration information at different stages. For example, for merchants in the non-primary stage, historical data can be analyzed to determine activity patterns. For merchants in the primary stage, the configuration information can be determined based on recommended information from similar successful merchants. For example, if stationery merchant A is new to the e-commerce platform and stationery merchant B has good sales results among the stationery merchants on the e-commerce platform, then merchant A can use configuration information similar to that of merchant B for promotional applications.

[0130] According to the disclosed embodiments, different configuration strategies are used to determine the configuration information of a target entity based on its different operating parameters. By flexibly adjusting the configuration information at different stages, the entity can not only better respond to market changes, but also maximize resource utilization efficiency while maintaining user loyalty.

[0131] According to an embodiment of the present disclosure, configuration information for a target entity is determined based on working parameters, including: when the working parameters do not meet preset conditions, obtaining an associated entity, the associated entity having the same second tag as the target entity, and the associated entity meeting a preset condition, the preset condition indicating that the entity has historical data; and determining configuration information for the target entity based on the historical configuration information of the associated entity.

[0132] The precondition can be whether the entity is in a non-primary stage. For example, if stationery merchant A is entering the e-commerce platform for the first time, then stationery merchant A does not meet the precondition.

[0133] The associated entity and the target entity have the same descriptive features. For example, Stationery Merchant A and Stationery Merchant B share the same descriptive feature: stationery.

[0134] In one example, consider stationery merchant A, which is new to the e-commerce platform. Stationery merchant B, a long-time e-commerce merchant, boasts strong sales performance among stationery merchants on the platform. Analysis of merchant B's historical data reveals that its coupon-based promotion model is most effective. Stationery merchant A can then employ similar configurations as merchant B: using coupons, identifying target users, and conducting promotions.

[0135] In another example, merchants use the platform for live streaming. The live streaming architecture pushes targeted demographic data based on configured support strategies, achieving intelligent and accurate results. The platform can configure coupons, which can be precisely distributed to all selected users. When merchants cold-start live streaming, recommending their live streaming rooms and corresponding coupons can increase user conversion rates and greatly improve traffic utilization efficiency.

[0136] It is understood that by setting a preset condition, for a target entity that does not meet the preset condition, the configuration information of the target entity can be determined based on the historical configuration information of the associated entities with the same second tag as the target entity. This not only helps the target entity quickly filter out target objects, but also improves the accuracy of the target entity's target object recommendations.

[0137] According to an embodiment of the present disclosure, the method also includes: obtaining weight values ​​corresponding to each activity mode in the configuration information; obtaining interaction data and conversion data for each activity mode, the interaction data including the number of interactions between the object and the target entity through the corresponding activity mode, and the conversion data including the number of objects participating in the activity mode; determining the contribution value of each activity mode to the conversion data based on the interaction data, conversion data and weight values; and updating the configuration information of the target entity based on the contribution value.

[0138] Weights can be used to represent the importance of different activity modes. For example, for Merchant A, three activity modes are configured: Activity 1: Playing Merchant A's advertisement on the app launch screen, corresponding to a first weight of 0.5; Activity 2: Promoting Merchant A through media bloggers, corresponding to a second weight of 0.3; Activity 3: Distributing coupons, corresponding to a third weight of 0.2. Weights can be determined through expert experience or generated based on a model.

[0139] Interaction data can be the number of interactions a user has with a target entity through a certain activity mode. For example, a user views merchant A by clicking on an ad in activity mode 1.

[0140] Conversion data can be the number of users who participated in a particular activity mode through a specific activity mode. Participation in an activity mode can mean that the user met the conversion metric. Conversion metrics can be key indicators for measuring merchant performance, such as sales, number of orders, user registrations, and purchase frequency. For example, if a user clicks on an ad in Activity Mode 1, views a merchant, and then purchases a product from Merchant A, that user will be counted in the conversion data.

[0141] When the conversion data is an ordering behavior, the interaction data may include multiple interaction modes: at least one of browsing behavior, collection behavior, adding to shopping cart behavior, consultation behavior, etc.

[0142] The contribution value may be the degree to which different activity modes contribute to the conversion effect of the target entity.

[0143] Based on the weight values ​​of different interaction modes in different activity modes, interaction data and conversion data, the weighted attribution method can be used to calculate the contribution value of each activity mode to the conversion effect of the target entity.

[0144] According to the embodiment of the present disclosure, by analyzing different activity patterns in the configuration information and determining the impact of each activity pattern on the conversion of the number of target entity users, it can help merchants analyze the results of the activity patterns and obtain a more effective target entity information recommendation method for objects.

[0145] In an e-commerce application, for a demographic package with high conversion rates and a small target audience, the descriptive features of the target entity can be used as public demographic data with these descriptive features. This can be used to serve different merchants in the same industry. Attribution analysis can be performed based on the different characteristics of merchants within the same industry, and the demographic package can be continuously iterated. Ultimately, a universal demographic package can be formed across the industry, further improving the efficiency of industry support.

[0146] In an embodiment of the present disclosure, the second dashboard can be generated based on the results of attribution analysis of each activity pattern. Figure 7 For example, the second dashboard 602 displays the attribution analysis results.

[0147] Figure 7 The figure schematically shows a second signboard 602 according to an embodiment of the present disclosure.

[0148] like Figure 7 As shown, the interaction data, conversion data, and attribution values ​​corresponding to different activity modes (eg, activity mode 1, activity mode 2, and activity mode 3) can be displayed to merchants in the form of a data table to form a second dashboard 602 .

[0149] It is understandable that by setting up a second dashboard, the effects of different activity modes on user conversion can be intuitively displayed to merchants.

[0150] It can be understood that the above describes the method of the present disclosure, and the following will describe the device of the present disclosure.

[0151] Figure 8 is a block diagram of an information recommendation device according to an embodiment of the present disclosure.

[0152] like Figure 8 As shown, the apparatus 700 may include a first determination module 710 , a second determination module 720 and a push module 730 .

[0153] The first determining module 710 is configured to determine entity features for the target entity and pattern features of corresponding activity patterns according to configuration information of the target entity, wherein the configuration information represents at least one activity pattern of the target entity with respect to an object.

[0154] The second determining module 720 is configured to determine a target object set corresponding to the target entity based on the interest of multiple objects with respect to the target entity, wherein the target object set includes at least one target object whose interest with respect to the target entity is greater than an interest threshold.

[0155] The push module 730 is configured to push a target entity and a corresponding activity mode to at least one target object in the target object set.

[0156] According to an embodiment of the present disclosure, the second determination module includes a first acquisition submodule, a first determination submodule, and a second determination submodule.

[0157] The first acquisition submodule is used to acquire at least one target feature, where the target feature includes an entity feature and a pattern feature.

[0158] The first determining submodule is configured to determine, based on at least one target feature, from a plurality of object sets a candidate object set associated with the at least one target feature, wherein the candidate object set includes at least one candidate object.

[0159] The second determination submodule is configured to determine the candidate object as the target object when the interest level of the candidate object for the target entity is greater than an interest level threshold.

[0160] According to an embodiment of the present disclosure, the apparatus may further include a first acquisition module, a first generation module, a second generation module, and a third determination module.

[0161] The first acquisition module is used to acquire first data of multiple objects and second data of multiple entities, the first data represents description information and behavior information of the objects, and the second data represents description information of the entities.

[0162] The first generating module is configured to generate at least one first label for each object based on the first data, where the first label includes at least one of a descriptive feature and a preference feature of the object.

[0163] The second generating module is used to generate at least one second label for each entity according to the second data, where the second label includes entity features and pattern features of the entity.

[0164] The third determination module is used to determine multiple object sets based on the first label and the second label, where the object sets represent the interest of the object with the first label in the entity with the second label.

[0165] According to an embodiment of the present disclosure, the third determination module may include a third determination submodule, a fourth determination submodule, and a fifth determination submodule.

[0166] The third determining submodule is configured to perform a cross analysis on the first tag and the second tag to determine a correlation between the first tag and the second tag.

[0167] The fourth determining submodule is configured to determine an association feature between the first tag and the second tag when the association degree is greater than a preset threshold.

[0168] The fifth determining submodule is configured to determine a plurality of object sets according to the associated features, the first label, and the second label.

[0169] According to an embodiment of the present disclosure, each object set in the plurality of object sets includes at least one object associated with the same entity and the interest level of the at least one object in the same entity. The apparatus further includes: a processing module.

[0170] The processing module is configured to input the associated features, the first label and the second label into an interest determination model to obtain the interest of at least one object with the first label to at least one entity with the second label.

[0171] According to an embodiment of the present disclosure, the first generation module further includes at least one of the following: a first generation submodule, a second generation submodule, or a third generation submodule.

[0172] The first generating submodule is used to generate an attribute label and a first model label of the object according to the first data. The attribute label includes at least one descriptive feature of the object, and the first model label includes a model type and model parameters used to generate the attribute label.

[0173] The second generating submodule is used to generate a statistical label and a second model label of the object according to the first data, wherein the statistical label includes at least one statistical feature of the object, and the second model label includes a model type and model parameters used to generate the statistical label.

[0174] The third generating submodule is used to generate a preference label and a third model label of the object based on the first data. The preference label includes the object's preference features for different entities, and the third model label includes the model type and model parameters used to generate the preference features.

[0175] According to an embodiment of the present disclosure, the device further includes: a second acquisition module and a fourth determination module.

[0176] The second acquisition module is used to acquire the working parameters of the target entity.

[0177] The fourth determining module is used to determine configuration information corresponding to the target entity according to the working parameters.

[0178] According to an embodiment of the present disclosure, the fourth determination module includes: a second acquisition submodule and a sixth determination submodule.

[0179] The second acquisition submodule is used to acquire an associated entity when the working parameter does not meet the preset condition, the associated entity has the same second label as the target entity, and the associated entity meets the preset condition, and the preset condition indicates that the entity has historical data.

[0180] The sixth determining submodule is configured to determine configuration information for the target entity based on the historical configuration information of the associated entity.

[0181] According to an embodiment of the present disclosure, the apparatus further includes: a third acquisition module, a fourth acquisition module, a fifth determination module, and an update module.

[0182] The third acquisition module is used to obtain the weight value corresponding to each activity mode in the configuration information.

[0183] The fourth acquisition module is used to obtain interaction data and conversion data of each activity mode, where the interaction data includes the number of interactions between the object and the target entity through the corresponding activity mode, and the conversion data includes the number of objects participating in the activity mode.

[0184] The fifth determining module is configured to determine a contribution value of each activity pattern to the conversion data based on the interaction data, the conversion data, and the weight value.

[0185] The update module is used to update the configuration information of the target entity according to the contribution value.

[0186] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0187] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0188] In some embodiments, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method provided by the present disclosure (e.g., method 200 described above).

[0189] In some embodiments, a non-transitory computer-readable storage medium stores computer instructions, which are used to enable a computer to execute a method provided according to the present disclosure (such as the above-mentioned method 200).

[0190] In some embodiments, a computer program product includes a computer program that implements the method provided by the present disclosure (such as the above-mentioned method 200) when executed by a processor. Figure 9 Further explanation is provided.

[0191] Figure 9 is a block diagram of an electronic device to which an information recommendation method can be applied according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 assistants, 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 claimed herein.

[0192] like Figure 9 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 500. Computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0193] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0194] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the information recommendation method. For example, in some embodiments, the information recommendation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the information recommendation method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the information recommendation method in any other appropriate manner (eg, by means of firmware).

[0195] Various embodiments of the systems and techniques described 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 parts (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0196] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0197] 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 apparatus. 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 apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0198] 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 cathode ray tube (CRT) display or a liquid crystal display (LCD)) 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).

[0199] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0200] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0201] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0202] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. An information recommendation method, comprising: Determining, based on configuration information of a target entity, an entity feature and a pattern feature of a corresponding activity pattern for the target entity, wherein the configuration information represents at least one activity pattern of the target entity with respect to an object, the configuration information includes a recommended mode for the target entity, the activity pattern is information corresponding to the recommended mode for the target entity, and the pattern feature is descriptive information of the activity pattern; Determining a target object set corresponding to the target entity based on the interest of multiple objects with respect to the target entity, the target object set including at least one target object, the interest of the target object with respect to the target entity being greater than an interest threshold, comprising: acquiring at least one target feature, the target feature including the entity feature and the pattern feature; determining a candidate object set associated with the at least one target feature from multiple object sets based on the at least one target feature, the candidate object set including at least one candidate object; and determining the candidate object as the target object if the interest of the candidate object with respect to the target entity is greater than the interest threshold; The target entity and the corresponding activity pattern are pushed to at least one target object in the target object set.

2. The method according to claim 1, wherein The plurality of object sets are determined by the following operations: Acquire first data of a plurality of objects and second data of a plurality of entities, wherein the first data represents description information and behavior information of the objects, and the second data represents description information of the entities; generating at least one first label for each of the objects based on the first data, the first label comprising at least one of a descriptive feature and a preference feature of the object; generating at least one second label for each of the entities based on the second data, the second label comprising entity features and pattern features of the entity; A plurality of object sets are determined based on the first label and the second label, where the object sets represent the interest of objects with the first label in an entity with the second label.

3. The method according to claim 2, wherein: Determining a plurality of object sets according to the first label and the second label includes: Performing a cross analysis on the first label and the second label to determine a correlation between the first label and the second label; When the correlation degree is greater than a preset threshold, determining a correlation feature between the first tag and the second tag; A plurality of object sets are determined according to the association feature, the first label, and the second label.

4. The method according to claim 3, wherein: Each object set in the plurality of object sets includes at least one object associated with a same entity and the interest level of the at least one object in relation to the same entity; the interest level is obtained by: The associated features, the first label, and the second label are input into an interest determination model to obtain the interest of at least one object with the first label to at least one entity with the second label.

5. The method according to claim 2, wherein: Generating at least one first label for each of the objects according to the first data includes at least one of the following: generating an attribute label and a first model label of the object according to the first data, wherein the attribute label includes at least one descriptive feature of the object, and the first model label includes a model type and model parameters used to generate the attribute label; generating a statistical label and a second model label of the object according to the first data, wherein the statistical label includes at least one statistical feature of the object, and the second model label includes a model type and model parameters used to generate the statistical label; Based on the first data, a preference label and a third model label of the object are generated, wherein the preference label includes the object's preference characteristics for different entities, and the third model label includes the model type and model parameters used to generate the preference characteristics.

6. The method according to claim 2, wherein: The configuration information is determined using the following operations: Obtaining working parameters of the target entity, wherein the working parameters are different stages of the entity's work; Configuration information corresponding to the target entity is determined according to the operating parameters.

7. The method according to claim 6, wherein: The determining, based on the operating parameters, configuration information for the target entity includes: When the working parameter does not satisfy a preset condition, obtaining an associated entity, wherein the associated entity has the same second tag as the target entity and the associated entity satisfies a preset condition, wherein the preset condition indicates that the entity has historical data; Configuration information for the target entity is determined based on the historical configuration information of the associated entity.

8. The method according to claim 1, further comprising: Obtaining a weight value corresponding to each of the activity modes in the configuration information; Obtaining interaction data and conversion data for each of the activity modes, wherein the interaction data includes the number of interactions between an object and the target entity through the corresponding activity mode, and the conversion data includes the number of objects participating in the activity mode; determining a contribution value of each activity pattern to the conversion data according to the interaction data, the conversion data, and the weight value; The configuration information of the target entity is updated according to the contribution value.

9. An information recommendation device, comprising: a first determining module, configured to determine, based on configuration information of a target entity, an entity feature of the target entity and a pattern feature of a corresponding activity pattern, wherein the configuration information represents at least one activity pattern of the target entity with respect to an object, the configuration information includes a recommended method for the target entity, the activity pattern is information corresponding to the recommended method for the target entity, and the pattern feature is descriptive information of the activity pattern; a second determination module, configured to determine, based on the interest levels of a plurality of objects with respect to the target entity, a target object set corresponding to the target entity, the target object set including at least one target object, the interest level of the target object with respect to the target entity being greater than an interest level threshold, comprising: acquiring at least one target feature, the target feature including the entity feature and the pattern feature; determining, based on the at least one target feature, a candidate object set associated with the at least one target feature from a plurality of object sets, the candidate object set including at least one candidate object; and determining the candidate object as the target object if the interest level of the candidate object with respect to the target entity is greater than the interest level threshold; a push module, configured to push the target entity and the corresponding activity mode to at least one target object in the target object set; The second determining module includes: A first acquisition submodule is configured to acquire at least one target feature, wherein the target feature includes the entity feature and the pattern feature; A first determining submodule is configured to determine, based on the at least one target feature, a candidate object set associated with the at least one target feature from a plurality of object sets, wherein the candidate object set includes at least one candidate object; The second determining submodule is configured to determine that the candidate object is a target object if the interest level of the candidate object for the target entity is greater than the interest level threshold.

10. The device according to claim 9, wherein The device further comprises: A first acquisition module is configured to acquire first data of a plurality of objects and second data of a plurality of entities, wherein the first data represents description information and behavior information of the objects, and the second data represents description information of the entities; a first generating module, configured to generate at least one first label for each of the objects based on the first data, wherein the first label includes at least one of a descriptive feature and a preference feature of the object; a second generating module, configured to generate at least one second label for each of the entities based on the second data, wherein the second label includes entity features and pattern features of the entity; The third determining module is configured to determine a plurality of object sets according to the first label and the second label, wherein the object sets represent the interest of the objects having the first label in the entity having the second label.

11. The device according to claim 10, wherein The third determining module includes: a third determining submodule, configured to perform a cross analysis on the first label and the second label to determine a correlation between the first label and the second label; A fourth determining submodule, configured to determine an association feature between the first tag and the second tag when the degree of association is greater than a preset threshold; A fifth determining submodule is configured to determine a plurality of object sets according to the associated features, the first label, and the second label.

12. The device according to claim 11, wherein Each object set in the plurality of object sets includes at least one object associated with a same entity and an interest level of the at least one object in the same entity, the apparatus further comprising: The processing module is configured to input the associated features, the first label and the second label into an interest determination model to obtain the interest of at least one object with the first label to at least one entity with the second label.

13. The device according to claim 10, wherein The first generation module further includes at least one of the following: a first generating submodule, configured to generate an attribute label and a first model label of the object based on the first data, wherein the attribute label includes at least one descriptive feature of the object, and the first model label includes a model type and model parameters used to generate the attribute label; a second generating submodule, configured to generate a statistical label and a second model label for the object based on the first data, wherein the statistical label includes at least one statistical feature of the object, and the second model label includes a model type and model parameters used to generate the statistical label; The third generation submodule is used to generate a preference label and a third model label of the object based on the first data, wherein the preference label includes the preference characteristics of the object for different entities, and the third model label includes the model type and model parameters used to generate the preference characteristics.

14. The device according to claim 10, wherein The device further comprises: A second acquisition module is used to acquire the operating parameters of the target entity; The fourth determining module is configured to determine configuration information corresponding to the target entity according to the operating parameters.

15. The device according to claim 14, wherein The fourth determining module includes: a second acquisition submodule, configured to acquire, if the working parameter does not satisfy a preset condition, an associated entity, wherein the associated entity has the same second tag as the target entity and satisfies a preset condition, wherein the preset condition indicates that the entity has historical data; The sixth determining submodule is configured to determine configuration information for the target entity based on the historical configuration information of the associated entity.

16. The device according to claim 9, wherein The device further comprises: A third acquisition module is used to obtain the weight value corresponding to each of the activity modes in the configuration information; a fourth acquisition module, configured to acquire interaction data and conversion data for each of the activity modes, wherein the interaction data includes the number of interactions between the object and the target entity through the corresponding activity mode, and the conversion data includes the number of objects participating in the activity mode; a fifth determining module, configured to determine a contribution value of each activity pattern to the conversion data based on the interaction data, the conversion data, and the weight value; An updating module is used to update the configuration information of the target entity according to the contribution value.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, 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 to 8.

19. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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