Collaborative filtering recommendation algorithm based on graph convolution attention mechanism

A collaborative filtering recommendation and attention technology, applied in computing, neural learning methods, special data processing applications, etc., can solve problems such as unfavorable expansion and limited model generalization ability

Pending Publication Date: 2021-06-04
LIAONING TECHNICAL UNIVERSITY
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However, the way they aggregate the features of neighbor nodes depends entirely on the graph structure, which

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  • Collaborative filtering recommendation algorithm based on graph convolution attention mechanism
  • Collaborative filtering recommendation algorithm based on graph convolution attention mechanism
  • Collaborative filtering recommendation algorithm based on graph convolution attention mechanism

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[0036] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION The specific embodiments of the present invention will be described in connection with the accompanying drawings, and as part of the present specification, the principles of the present invention will be described, and other aspects, features, and advantages of the present invention will become an eye. In the accompanying drawings, the same or similar components in different figures are represented by the same reference numerals.

[0037] The present invention is an effective modeling for the different influences of the prior art unacceptable to polymerize the neighbor node. Figure 1 ~ 6 A specific design structure of data processing and a map volume focused mechanism network model is given.

[0038] The first step is to build a user-project interaction. First, the user acquired by the data is processed with the project interaction data, and the processing of the process is constructed as non-European spatial...

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Abstract

The invention discloses a collaborative filtering recommendation algorithm based on a graph convolution attention mechanism. The method comprises the steps of firstly collecting and processing data and dividing a data set, secondly constructing a GACF model, and finally training the model and recommending by predicting an association score between a user and an item. According to the graph convolution attention mechanism collaborative filtering model provided by the invention, firstly, user-project interaction information is mapped to a vector space by using a graph embedding technology, then, the embedding expression of a user-project interaction graph is learned through a graph convolution network, and then, different weights are allocated to neighbor nodes by using an attention mechanism. By aggregating the feature information of the neighbor nodes, the weight between the neighbor nodes can only depend on the feature expression between the nodes, so that the generalization ability of the model is improved, and finally, a plurality of embedded vectors learned by a graph convolution layer are weighted and aggregated to obtain the association score between the user and the project.

Description

Technical field [0001] The present invention belongs to the technical field of computer artificial intelligence, and in particular, the present invention relates to a synergistic filtration recommendation algorithm based on the graphic volume attention mechanism. Background technique [0002] In this era of this data large explosion, in order to relieve information overload issues, the recommended system has been widely used in personalized information filtration. The most widely recommended technique for application is a collaborative filtering recommendation algorithm. It is based on similar users to exhibit similar preferences, and explore users's hidden preferences through user history behavior, and perform implicit preferences according to user implicit preferences recommend. Early recommended models directly use the ID number of users and projects as embedded vectors, resulting in limitations of embedded expression. Subsequently, many researchers increase user IDs and its i...

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Application Information

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IPC IPC(8): G06F16/9535G06N3/04G06N3/08
CPCG06F16/9535G06N3/08G06N3/045
Inventor 孟祥福朱金侠邢长征朱尧薛琪孙德伟王丹丹
Owner LIAONING TECHNICAL UNIVERSITY
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