Personalized recommendation method based on knowledge graph convolution algorithm

Abstract

The invention discloses a personalized recommendation method based on a knowledge graph convolution algorithm, and belongs to the field of hybrid recommendation systems. The method comprises the following steps: firstly, constructing a 1-to-d-hop entity receptive field set of each article in a domain knowledge graph, aggregating embedded vectors of d-to-1-hop receptive field entities by using a graph convolution algorithm, and calculating an article embedded vector containing high-order neighbor information; in the aggregation process, on the basis of the graph convolution algorithm, using a knowledge graph for representing a learning model DistMult and representing an attention mechanism of user interest distribution to improve the embedded vector expression ability; by referring to a matrix decomposition model, multiplying a high-order article embedding vector generated by iteration with a user embedding vector, and outputting a predicted interaction probability by using a Sigmoid function; and inferring a combined loss function of a hybrid user article interaction matrix loss function and a knowledge graph representation learning loss function by using maximum posteriori probability estimation. According to the method, the high-order feature representation of an article embedding vector is enriched, and the expression accuracy of the entity and relationship embedding vectoris enhanced.

Description

technical field [0001] The invention belongs to the field of hybrid recommendation systems, and in particular relates to a personalized recommendation model based on a knowledge map convolution algorithm. Background technique [0002] In recent years, the vigorous development of science and technology, especially the Internet, has caused human society to face a serious "information overload" problem. How to more effectively display the most attractive content on limited Internet pages has become a hot spot pursued by major technology companies. In this process, various recommendation algorithms have made great progress. The recommendation algorithm intends that for a given user-item pair, first estimate the score that the user may interact with the item, then sort the items based on the score, and finally recommend the TopN items to the user. The classic collaborative filtering recommendation algorithm attempts to jointly model the user's interest characteristics and item a...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the current graph convolutional neural network model based on knowledge graphs is too simple to process when aggregating neighborhood information, and the vector representation of entities

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