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Knowledge graph-based user group recommendation system and method

A knowledge map and user group technology, applied in neural learning methods, relational databases, special data processing applications, etc., can solve problems such as inability to directly obtain group preferences, few interaction records between groups and items, etc.

Active Publication Date: 2021-02-12
四川省人工智能研究院(宜宾)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for sporadic groups, members seldom get together, and there are too few interaction records between groups and items (data sparsity problem), so it is impossible to directly obtain group preferences. Traditional collaborative filtering algorithms are also limited by data sparsity. sexual problems

Method used

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  • Knowledge graph-based user group recommendation system and method
  • Knowledge graph-based user group recommendation system and method
  • Knowledge graph-based user group recommendation system and method

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Embodiment Construction

[0069] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0070] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0071] Such as figure 1 As shown in , a user group recommendation system based on knowledge graph, including sequentially connected information dissemination module, preference aggregation module and optimization module;

[0072] The information dissemination module is used to build a collaborative...

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Abstract

The invention discloses a knowledge graph-based user group recommendation system and method, and the method comprises the following steps: constructing a collaborative knowledge graph, aggregating node neighbors to obtain the highest-order representation of nodes in the collaborative knowledge graph, and then obtaining user group and article features by highest-order representation and an attention mechanism, constructing a score prediction function; and constructing a loss function according to the score prediction function to update the learnable parameters, obtaining prediction scores of the articles according to the updated learnable parameters, and recommending a plurality of articles with the highest prediction scores to the user group. When the articles are recommended to the user group, the problem of data sparseness can be avoided, and the articles in which the user is interested can be accurately recommended.

Description

technical field [0001] The invention belongs to the field of recommendation systems, and in particular relates to a user group recommendation system and method based on knowledge graphs. Background technique [0002] With the advent of the era of big data, data began to explode. In order to solve the problem of information overload, recommender systems are widely used in online services such as e-commerce, content sharing, social networking, and forums. With the advent of the era of mobile Internet access, people can more and more conveniently gather online for group activities such as eating, watching movies or traveling. These people may know each other well, such as in a family or have several mutual friends. A user group can also be a group of people who meet by chance at an event, such as several travelers joining a tour group. At this time, the recommendation system needs to recommend one or several suitable items for this group of people. In addition to recommende...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/28G06F16/36G06N3/04G06N3/08
CPCG06F16/9536G06F16/288G06F16/367G06N3/08G06N3/045
Inventor 邵杰邓智毅李长宇刘树锦申恒涛
Owner 四川省人工智能研究院(宜宾)
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