Information recommendation method, device, equipment, storage medium and program product

Abstract

This disclosure provides a method, apparatus, device, storage medium, and program product for information recommendation, relating to the field of computer technology, and particularly to the field of intelligent recommendation technology. The specific implementation involves: determining the numerical values ​​of attribute tags for category information based on the object's historical behavioral data, where the numerical values ​​of the attribute tags characterize the object's degree of need for the category information; and recommending information related to the category information to the object based on the numerical values ​​of the attribute tags.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to the field of intelligent recommendation technology. Specifically, it relates to an information recommendation method, an information recommendation device, an electronic device, a non-transitory computer-readable storage medium, and a computer program product. Background Technology

[0002] Information recommendations can be triggered by keywords in the search results for an object. This means that the information recommendations are based solely on the object's search content, ignoring the object's own characteristics, which leads to inaccurate information recommendations. Summary of the Invention

[0003] This disclosure provides a method, apparatus, device, storage medium, and program product for information recommendation.

[0004] According to one aspect of this disclosure, an information recommendation method is provided, comprising: determining, based on historical behavioral data of an object, the numerical values ​​of attribute tags for category information of the object, wherein the numerical values ​​of the attribute tags characterize the degree of need of the object for the category information; and recommending information related to the category information to the object based on the numerical values ​​of the attribute tags.

[0005] According to another aspect of this disclosure, an information recommendation apparatus is provided, comprising: a determining module and a recommending module. The determining module is configured to determine, based on historical behavioral data of an object, the numerical values ​​of attribute tags for category information, wherein the numerical values ​​of the attribute tags characterize the degree of need of the object for the category information. The recommending module is configured to recommend information related to the category information to the object based on the numerical values ​​of the attribute tags.

[0006] According to another aspect of this 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the information recommendation method described above.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the information recommendation method described above.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the information recommendation method described above.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0011] Figure 1 This is an exemplary system architecture applicable to information recommendation methods and apparatus according to embodiments of this disclosure;

[0012] Figure 2 This is a flowchart according to an embodiment of the present disclosure;

[0013] Figure 3 This is an example schematic diagram according to another embodiment of the present disclosure;

[0014] Figure 4 This is an example schematic diagram according to yet another embodiment of the present disclosure;

[0015] Figure 5 This is a block diagram of an information recommendation apparatus according to embodiments of the present disclosure; and

[0016] Figure 6 This is a block diagram of an electronic device that can implement the information recommendation method of the embodiments of this disclosure. Detailed Implementation

[0017] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0018] Figure 1 The schematic illustration shows the system architecture of an information recommendation method and apparatus according to an embodiment of the present disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0019] like Figure 1As shown, the system architecture 100 according to this embodiment may include clients 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between clients 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0020] Users can use clients 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on clients 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0021] Clients 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers. Clients 101, 102, and 103 in this embodiment of the disclosure can, for example, run applications.

[0022] Server 105 can be a server providing various services, such as a backend management server supporting websites browsed by users using clients 101, 102, and 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the clients. Alternatively, server 105 can also be a cloud server, meaning server 105 has cloud computing capabilities.

[0023] It should be noted that the information recommendation method provided in this embodiment can be executed by server 105. Correspondingly, the information recommendation device provided in this embodiment can be located in server 105. The information recommendation method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with clients 101, 102, 103 and / or server 105. Correspondingly, the information recommendation device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with clients 101, 102, 103 and / or server 105.

[0024] In one example, server 105 can obtain historical behavior data from users 101, 102, and 103 via network 104 and make information recommendations based on a certain category of historical behavior data.

[0025] It should be understood that Figure 1The number of clients, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of clients, networks, and servers.

[0026] This disclosure provides an information recommendation method, which will be described below in conjunction with... Figure 1 The system architecture, referencing Figures 2-4 This describes an information recommendation method according to exemplary embodiments of the present disclosure. The information recommendation method of the embodiments of the present disclosure may, for example, be derived from... Figure 1 The server 105 shown is used to execute this.

[0027] It is understood that the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0028] Figure 2 A flowchart illustrating an information recommendation method according to an embodiment of the present disclosure is shown schematically.

[0029] like Figure 2 As shown, the information recommendation method 200 of this embodiment may include, for example, operations S210 to S220.

[0030] In operation S210, based on the object's historical behavior data, the numerical values ​​of the attribute labels for the object's category information are determined. The numerical values ​​of the attribute labels represent the degree to which the object requires category information.

[0031] In operation S220, information related to category information is recommended to the object based on the value of the attribute label.

[0032] It should be understood that an object's historical behavior data characterizes the object's own characteristics and indirectly reflects the object's needs. Based on this, embodiments of this disclosure use the object's historical behavior data for a certain type of information and tags that characterize its needs, i.e., attribute tags, to represent the degree of the object's need for a certain type of information using the numerical value of the attribute tags.

[0033] In the embodiments of this disclosure, when making information recommendations, the numerical values ​​of the attribute tags of an object for category information can be combined with the object's own characteristics to quantify the object's need for information of a certain category, thereby accurately recommending information related to the category information to the object.

[0034] This embodiment of the disclosure uses information recommendation in the medical aesthetics category as an example. Historical behavioral data related to the medical aesthetics category can be analyzed from an object's historical behavioral data, and the attribute tag value of the medical aesthetics category can be obtained based on this historical behavioral data. This value represents the object's degree of need for information in the medical aesthetics category. Of course, the information recommendation method 200 of this embodiment can also be applied to information recommendation in other categories, and is not limited thereto.

[0035] For example, if users A and B both search for the search term "cosmetic procedure C," and their historical behavioral data shows different levels of demand, their attribute label values ​​related to the cosmetic procedure category will differ. Therefore, the cosmetic procedure category information recommended to users A and B can also be differentiated to suit their different levels of demand. For example, the differentiation could be achieved by varying the number of recommended recommendations.

[0036] Figure 3 A schematic diagram of an information recommendation method 300 according to another embodiment of the present disclosure is shown.

[0037] like Figure 3 As shown, operation S310, which determines the value of the attribute label for category information based on the object's historical behavior data, may include: obtaining a first value D1 based on the historical cumulative values ​​related to category D in the object's historical behavior data, which serves as an influencing factor on the value E of the attribute label for category D; then obtaining a second value D2 based on the number of clicks the object makes on information related to category D within a specified time period, which serves as another influencing factor on the value E of the attribute label for category D; and finally determining the value E of the attribute label based on the first value D1 and the second value D2.

[0038] It should be noted that the "specified time period" mentioned above can be any selected historical time period, or it can be a real-time time period with the current time as the end point and a set time period length as the length. In order to understand the changes in the needs of the object in real time, the "specified time period" in this embodiment of the disclosure is the latter. In addition, the above-mentioned "determine the value E of the attribute label according to the first value D1 and the second value D2" can be, for example, by summing the first value D1 and the second value D2 to obtain the value E of the attribute label.

[0039] According to embodiments of this disclosure, by obtaining a first value from the object's historical behavior data and based on a second value reflecting the change in the object's demand for information of that category over a certain period of time, the value E of the attribute label can be accurately determined, that is, the object's demand for information of a certain category can be accurately quantified.

[0040] like Figure 3As shown, historical behavioral data for category D may include consumer behavior data D. 1x Consultation behavior data D 1y And browsing behavior data D 1z In operation S310, based on the historical cumulative values ​​related to category D in the object's historical behavior data, the first value D1 is obtained. The first weight, the second weight, and the third weight are used respectively to obtain the weighted sum of the historical cumulative values ​​of consumption behavior data, consultation behavior data, and browsing behavior data, which is used as the first value D1.

[0041] For example, the values ​​of the first weight, the second weight, and the third weight decrease sequentially.

[0042] It is understandable that different historical behavioral data reflect different levels of demand from different individuals. For example, generally, consumer behavior data reflects the highest level of demand, followed by consultation behavior data, and browsing behavior data reflects the lowest. Therefore, through the embodiments of this disclosure, different weights can be assigned to different historical behavioral data of an individual, allowing for a more accurate quantification of the individual's demand for a particular category of information.

[0043] like Figure 3 As shown, in the information recommendation method 300 according to an embodiment of this disclosure, in operation S320, recommending category-related information to an object based on the value of the attribute tag may include: recommending category-related information to the object when the value of the attribute tag E is greater than or equal to the recommendation threshold Th.

[0044] For example, the operation S320 of recommending information related to category information to an object based on the value of the attribute label may further include: determining the number of information to be recommended based on the difference between the value E of the attribute label and the recommendation threshold Th.

[0045] It should be understood that the information recommendation method in this disclosure quantifies an object's need for information of a certain category, so that the numerical value of the attribute tag represents the degree of the object's need for information of that category. After obtaining the numerical value of the attribute tag, the magnitude of the attribute tag value is used to determine whether to recommend information of that category and, if so, to determine the amount of recommended information.

[0046] When the value of an attribute tag does not reach the recommendation threshold, it indicates that the object has a low demand for information in that category, and information related to that category may not be recommended to the object. Especially for information such as advertisements, if the object's demand is low, recommending related information may be met with resistance, potentially having a negative effect. In this embodiment, when the value of an attribute tag reaches the recommendation threshold, the difference between the attribute tag value and the recommendation threshold indicates that the object's demand for information in that category meets the standard for recommended information. Furthermore, the magnitude of the difference, representing the degree of demand, is positively correlated with the number of information recommendations for that category. This allows for adaptive adjustments to information recommendations based on the object's varying degrees of demand.

[0047] Figure 4 A schematic diagram of an information recommendation method 400 according to yet another embodiment of the present disclosure is shown.

[0048] like Figure 4 As shown, historical behavior data may also include historical search data D0. In operation S410, determining the value of the attribute label of the object for category information based on the object's historical behavior data may include: obtaining the object's intent data F based on the historical search data D0; then obtaining the initial value E0 of the attribute label based on the object's intent data F; then obtaining the second value D2 based on the number of clicks the object made on category information within a specified time period; and finally obtaining the value E of the attribute label based on the second value D2 and the initial value E0 of the attribute label.

[0049] It should be understood that historical search data can reflect an object's intent, which can indirectly reflect its needs. Taking the medical aesthetics category as an example, the "intent" here could be searching for institutions, seeking treatment plans, or inquiring about prices. Generally, the degree of need corresponding to the intents of searching for institutions, inquiring about prices, and finding treatment plans decreases in that order. Based on this, embodiments of this disclosure assign numerical values ​​to the intents reflected in historical search data. The degree of need reflected by the initial values ​​of attribute tags is used to indirectly quantify the intent. Furthermore, based on a second value reflecting the change in the object's need for information in that category over a certain period, the degree of need for a particular category of information can be accurately quantified, allowing for more accurate recommendations of that category of information to the object.

[0050] For example, intent data F can be obtained by performing intent analysis on historical search data D0 of an object using a natural language machine learning model. Those skilled in the art will understand that the machine learning model may include TextCNN, and this disclosure is not limiting in this regard.

[0051] like Figure 4As shown, after obtaining the attribute label value E in operation S410, the attribute label value E can be further compared with the recommendation threshold Th. When the attribute label value E is greater than the recommendation threshold Th, information related to the category information is recommended to the object. For example, the difference between the attribute label value E and the recommendation threshold Th can also be positively correlated with the amount of recommended information, thereby enabling adaptive adjustment of information recommendations based on different needs of the object.

[0052] It should be noted that, Figure 4 In the illustrated embodiment, the object's intent data F can also be used as a new attribute label (e.g., an intent attribute label), with the intent data corresponding to the value of this attribute label. The number of clicks the object makes on category information within a specified time period also affects the value of this attribute label, which represents the object's level of demand for that category of information.

[0053] like Figure 5 As shown, the image recognition device 500 of this embodiment includes, for example, a determination module 510 and a recommendation module 520.

[0054] The determining module 510 can be used to determine the numerical values ​​of the attribute labels of an object for category information based on the object's historical behavior data. The numerical values ​​of the attribute labels represent the degree to which the object requires category information. According to embodiments of this disclosure, the determining module 510 can, for example, perform the above-mentioned reference... Figure 2 The operation S210 described herein will not be repeated here.

[0055] Recommendation module 520 can be used to recommend information related to category information to an object based on the value of attribute tags. According to embodiments of this disclosure, recommendation module 520 can, for example, perform the functions described above. Figure 2 The operation S220 described herein will not be repeated here.

[0056] According to embodiments of this disclosure, the determining module includes: a first determining submodule, a second determining submodule, and a third determining submodule. The first determining submodule is used to obtain a first value based on the historical cumulative values ​​related to the category information in the object's historical behavior data. The second determining submodule is used to obtain a second value based on the number of clicks the object makes on the category information within a specified time period. The third determining submodule is used to determine the value of the attribute tag based on the first and second values.

[0057] According to embodiments of this disclosure, the historical behavior data includes consumption behavior data, consultation behavior data, and browsing behavior data; the first determining submodule includes a first determining unit. The first determining unit is used to obtain a weighted sum of the historical cumulative values ​​of the consumption behavior data, the historical cumulative values ​​of the consultation behavior data, and the historical cumulative values ​​of the browsing behavior data, respectively, using a first weight, a second weight, and a third weight, as the first value, wherein the values ​​of the first weight, the second weight, and the third weight decrease sequentially.

[0058] According to embodiments of this disclosure, the recommendation module includes a first recommendation submodule. The first submodule is used to recommend information related to the category information to the object when the value of the attribute tag is greater than or equal to a recommendation threshold.

[0059] According to embodiments of this disclosure, the recommendation module further includes a second recommendation submodule. The second recommendation submodule is used to determine the number of information items to be recommended based on the difference between the value of the attribute tag and the recommendation threshold.

[0060] According to embodiments of this disclosure, the historical behavior data includes historical search data; the determining module further includes: a fourth determining submodule, a fifth determining submodule, a sixth determining submodule, and a seventh determining submodule. The fourth determining submodule is used to obtain the object's intent data based on the historical search data. The fifth determining submodule is used to obtain the initial value of the attribute tag based on the object's intent data. The sixth determining submodule is used to obtain a second value based on the number of clicks the object makes on information of the category within a specified time period. The seventh determining submodule is used to obtain the value of the attribute tag based on the second value and the initial value of the attribute tag.

[0061] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0062] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0063] like Figure 6As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0064] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0065] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as information recommendation methods. For example, in some embodiments, the information recommendation method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the information recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform information recommendation methods by any other suitable means (e.g., by means of firmware).

[0066] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0067] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0068] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0069] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0070] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0071] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0072] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0073] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An information recommendation method, comprising: Based on the historical behavior data of an object for a single category of information, the numerical value of the attribute label of the object for the single category of information is determined, and the numerical value of the attribute label represents the degree of demand of the object for the single category of information; as well as Based on the numerical value of the attribute tag, recommend information related to the single category information to the object; The step of determining the value of the attribute tag of an object for a single category of information based on the object's historical behavior data includes: obtaining a first value based on the historical cumulative value related to the single category of information in the object's historical behavior data; obtaining a second value based on the number of clicks the object makes on the single category of information within a specified time period; the historical behavior data includes historical search data for the single category of information; obtaining the object's intent data based on the historical search data; obtaining the initial value of the attribute tag based on the object's intent data; determining the value of the attribute tag based on the initial value of the attribute tag, the first value, and the second value; the specified time period includes a real-time time period ending at the current time and having a set time period duration. The historical behavioral data includes consumption behavior data, consultation behavior data, and browsing behavior data; The step of recommending information related to the single category information to the object based on the value of the attribute tag includes: recommending information related to the single category information to the object when the value of the attribute tag is greater than or equal to a recommendation threshold, and determining the number of recommended information based on the difference between the value of the attribute tag and the recommendation threshold; wherein the magnitude of the difference represents the degree of demand and is positively correlated with the number of recommended information.

2. The method according to claim 1, wherein, The first value is obtained based on the historical cumulative value related to the single category information in the object's historical behavior data, including: Using the first weight, second weight, and third weight respectively, the weighted sum of the historical cumulative values ​​of the consumer behavior data, the historical cumulative value of the consultation behavior data, and the historical cumulative value of the browsing behavior data is obtained, and this sum is used as the first value. The values ​​of the first weight, the second weight, and the third weight decrease sequentially.

3. An information recommendation device, comprising: The determination module is used to determine the value of the attribute label of the object for the single category information based on the object's historical behavior data for the single category information. The value of the attribute label represents the degree of the object's need for the single category information. The recommendation module is used to recommend information related to the single category information to the object based on the value of the attribute tag; The determining module includes: The first determining submodule is used to obtain a first value based on the historical cumulative value related to the single category information in the object's historical behavior data; The second determining submodule is used to obtain a second value based on the number of clicks on the single category information by the object within a specified time period; the specified time period includes a real-time time period with the current time as the end point and a set time period duration as the length. The historical behavior data includes historical search data for the single category information; the fourth determining submodule is used to obtain the object's intent data based on the historical search data; the fifth determining submodule is used to obtain the initial value of the attribute tag based on the object's intent data; The third determining submodule is used to determine the value of the attribute label based on the initial value of the attribute label, the first value, and the second value; The historical behavioral data includes consumption behavior data, consultation behavior data, and browsing behavior data; The recommendation module includes: The first recommendation submodule is used to recommend information related to the category information to the object when the value of the attribute label is greater than or equal to the recommendation threshold. The second recommendation submodule is used to determine the number of recommended information based on the difference between the value of the attribute tag and the recommendation threshold; wherein the magnitude of the difference represents the degree of demand and is positively correlated with the number of recommended information.

4. The apparatus according to claim 3, wherein, The first determining submodule includes: The first determining unit is used to obtain a weighted sum of the historical cumulative values ​​of the consumer behavior data, the historical cumulative values ​​of the consultation behavior data, and the historical cumulative values ​​of the browsing behavior data using a first weight, a second weight, and a third weight, respectively, as the first value, wherein the values ​​of the first weight, the second weight, and the third weight decrease sequentially.

5. 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 to enable the at least one processor to perform the method of any one of claims 1-2.

6. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-2.

7. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-2.

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