Recommendation method for aggregating knowledge graph neural network and adaptive attention

Abstract

The invention provides a recommendation method for aggregating a knowledge graph neural network and adaptive attention, and the method comprises the following steps: S1, taking a knowledge graph triple of a user, a relationship and an entity as an input, and distributing initial embedding representations, namely a user embedding representation, a relationship embedding representation and an entity embedding representation, for the input; S2, using an inner product to represent the importance degree of the relationship to the user; converting the heterogeneous knowledge graph into a weighted graph, then selecting neighbor target nodes, and training domain embedding representation of the neighbor target nodes; feeding the initial entity embedding representation into a graph neural network for training and generating a new entity embedding representation; performing polymerization to obtain a final article embedding expression; and S3, taking an inner product of the user embedded representation and the final article embedded representation as a final prediction score, and recommending the article corresponding to the highest score to the user. According to the method, the limitation problem that a matrix decomposition algorithm only utilizes interaction between a user and an article is effectively solved, and the neighbor nodes are considered when the vector representation of the neighborhood of the target node is aggregated.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a recommendation method for aggregating a knowledge graph neural network and adaptive attention. Background technique [0002] In the era of information overload, recommender systems play a pivotal role in various online services, aiming to recommend items of interest to users. Matrix factorization, a popular technique used in recommender systems, uses the user-item rating matrix as input data to model user preferences as the inner product of user vectors and item vectors. However, ratings only reflect a user's overall opinion of an item, not a specific aspect of the item. Therefore, matrix factorization (MF) techniques cannot perform fine-grained modeling of user preferences from all aspects of items, nor can they explain recommendation results. [0003] In recent years, a lot of work on recommender systems based on Knowledge Graph (KG) has emerged, since KG con...

AI Technical Summary

Problems solved by technology

However, existing KG-based recommender systems cannot directly model relational vectors, nor can they visually display attention weights.

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