Collaborative knowledge perception enhanced network recommendation method

A technology to enhance network and recommendation methods, applied in instrumentation, unstructured text data retrieval, computing, etc., to enrich user and item feature modeling, improve model performance and generalization, and promote information sharing

Inactive Publication Date: 2021-09-21
XINJIANG UNIVERSITY
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems existing in the prior art, provide a collaborative knowledge perception enhanced network recommendation method, which can better adapt to data sparseness and user cold start problems, thereby improving model performance and generalization

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  • Collaborative knowledge perception enhanced network recommendation method
  • Collaborative knowledge perception enhanced network recommendation method
  • Collaborative knowledge perception enhanced network recommendation method

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Embodiment

[0022] Example: as attached figure 1 As shown, the collaborative knowledge perception enhanced network recommendation method of the present invention includes steps:

[0023] S1. Build a knowledge map;

[0024] S2. Seamlessly combine the explicitly coded collaborative information in the user-item interaction with the associated knowledge in the knowledge graph, and integrate the dissemination information of users and items in the knowledge graph through the knowledge-aware neural attention mechanism;

[0025] S3. Construct a cross-compression unit, obtain the original embedding of the project through the cross-compression unit, and then fuse it with the knowledge map dissemination information;

[0026] S4. Carry out score prediction.

[0027] Preferably, the step S1 specifically includes: simulating the dissemination of knowledge in the real world, and constructing a knowledge graph dissemination model according to the entity-relationship-entity dissemination mode.

[0028]...

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Abstract

The invention discloses a collaborative knowledge perception enhanced network recommendation method, and mainly relates to the technical field of computer application. Comprising the following steps: S1, constructing a knowledge graph; s2, seamlessly combining explicit coded collaborative information in interaction of the user and the project with associated knowledge in the knowledge graph, and fusing propagation information of the user and the project in the knowledge graph through a knowledge perception nerve attention mechanism; s3, constructing a cross compression unit, obtaining project original embedding through the cross compression unit, and fusing the project original embedding with knowledge graph propagation information; s4, performing score prediction; the invention can better adapt to the problems of data sparseness and user cold start, so that the model performance and generalization are improved.

Description

technical field [0001] The invention relates to the field of computer application technology, in particular to a collaborative knowledge perception enhanced network recommendation method. Background technique [0002] Recommender systems aim to solve the problem of information explosion and satisfy users' personalized interests. One of the most popular recommendation techniques is collaborative filtering, which utilizes users' historical interactions and makes recommendations based on their common preferences. However, collaborative filtering based methods usually suffer from the sparsity of user-item interactions as well as the cold start problem. Therefore, researchers propose to use auxiliary information in recommender systems, including social networks, attributes, and multimedia (e.g., text, images, etc.). Among various kinds of incidental information, knowledge graphs containing more facts and connections about facts have received increasing attention. The core of kn...

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Application Information

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IPC IPC(8): G06F16/9536G06F16/36
CPCG06F16/9536G06F16/367
Inventor 秦继伟曾威
Owner XINJIANG UNIVERSITY
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