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Cross-session recommendation method and system, storage medium and electronic equipment

A recommendation method and recommendation system technology, applied in the fields of electronic digital data processing, special data processing applications, instruments, etc., can solve the problems of insufficient user behavior data and item click record data, and achieve rich content recommendation materials and ease cold start. Effect

Pending Publication Date: 2021-09-28
SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Embodiments of the present application provide a cross-session recommendation method, system, storage medium, and electronic device to at least solve the problem of insufficient user behavior data and item click record data in existing cross-session recommendation methods

Method used

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  • Cross-session recommendation method and system, storage medium and electronic equipment
  • Cross-session recommendation method and system, storage medium and electronic equipment
  • Cross-session recommendation method and system, storage medium and electronic equipment

Examples

Experimental program
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Embodiment 1

[0050] The present invention provides a cross-session recommendation method. The main technical route of the present invention is as follows: figure 1 As shown, the specific steps are as follows:

[0051] Please refer to figure 2 , figure 2 is a flowchart of the cross-session recommendation method. Such as figure 2 As shown, the cross-session recommendation method of the present invention includes:

[0052] Conversation sequence construction step S1: preprocessing the original conversation text to obtain a conversation sequence.

[0053] Specifically, firstly, the original conversation text is preprocessed. The main purpose of this step is to construct the existing conversation text into a series of conversation sequences. As shown in Table 1, we get the conversation text of 3 users, Then according to the sequence of the sessions, we can get the session sequence representation shown in the last column of Table 1.

[0054] Conversation graph construction step S2: Const...

Embodiment 2

[0069] Please refer to Figure 4 , Figure 4 is a schematic structural diagram of the cross-session recommendation system of the present invention. Such as Figure 4 Shown is a cross-session recommendation system of the present invention, including:

[0070] A conversation sequence construction module, the conversation sequence construction module preprocesses the original conversation text to obtain a conversation sequence;

[0071] a session graph construction module, the session graph construction module constructs a session graph based on the session sequence;

[0072] A session sequence encoding module, configured to encode the session structure of the session graph to obtain a session vector;

[0073] A knowledge acquisition module, the knowledge acquisition module outputs conversational text knowledge through a knowledge acquisition model according to the conversational vector;

[0074] A prediction module, the prediction module obtains recommendation results relat...

Embodiment 3

[0081] combine Figure 5 As shown, this embodiment discloses a specific implementation manner of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.

[0082] Specifically, the processor 81 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

[0083] Among them, the memory 82 may include mass storage for data or instructions. For example without limitation, the memory 82 may include a hard disk drive (Hard Disk Drive, referred to as HDD), a floppy disk drive, a solid state drive (SolidState Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape or universal serial bus (Universal Serial Bus, referred to as USB) drive or a combination of two or more of the above. Storage 82 may comprise removable or n...

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Abstract

The invention discloses a cross-session recommendation method and system, a storage medium and electronic equipment, the cross-session recommendation method comprises the following steps: a session sequence construction step: preprocessing an original session text to obtain a session sequence; a session graph construction step: constructing a session graph based on the session sequence; a session sequence encoding step: encoding a session structure of the session graph to obtain a session vector; a knowledge acquisition step: outputting session text knowledge through a knowledge acquisition model according to the session vector; and a prediction step: obtaining a recommendation result related to user interests through a multilayer perceptron according to the session text knowledge. According to the method, local session text knowledge and global session text knowledge are considered, so that content recommendation materials are richer.

Description

technical field [0001] The invention belongs to the field of cross-session recommendation, and in particular relates to a cross-session recommendation method, system, storage medium and electronic equipment. Background technique [0002] In the recommendation system, user portrait data and item portrait data provide the basic recommendation basis. In addition, data such as user historical behavior data and item click records also play an extremely important role, providing more personalized recommendations. sufficient basis. With the maturity of recommendation system technology, data such as user behavior data and item click records have become the bottleneck for determining the recommendation effect. In other words, with richer data, the recommendation algorithm can play a better recommendation effect. Therefore, how to obtain and construct richer behavioral characteristic data is very important. [0003] Specific description of prior art: [0004] In the existing recom...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06N3/04
CPCG06F16/9536G06N3/049G06N3/045
Inventor 朱志强徐凯波
Owner SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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