An Interpretable Recommender System Based on Knowledge Graph

Abstract

The invention discloses an interpretable recommendation system based on knowledge graph, which includes a sequential knowledge graph construction module: extracting predefined entities and relationships from user purchase sequences and user comments; user representation learning module: designed A graph attention network to learn the user's short-term preference, redesign the structure of the gated recurrent unit so that it can integrate the user's long-term preference and short-term preference, and obtain a user vector representation that captures the dynamics of user preference; item representation learning module : Design a self-attention layer to learn the user-specific vector representation of the item; Rating prediction module: combine the vector representation of the user and the vector representation of the item to obtain the user's predicted rating for the item. Through the above solution, the present invention achieves high prediction accuracy, and at the same time provides very efficient explanation, and has high practical value and popularization value.

Description

technical field [0001] The invention belongs to the technical field of knowledge graphs, and in particular relates to an interpretable recommendation system based on knowledge graphs. Background technique [0002] In recent years, explainable recommender systems have received more and more attention in academia and industry. Related studies have shown that explainable recommender systems can not only improve users' acceptance of recommended items, but also improve the transparency and persuasion of recommender systems. power, efficiency, reliability and user satisfaction. [0003] In order to improve the interpretability of the recommendation system, most of the existing models use user comments containing rich semantic information as the input of the model. Although the interpretability of the model has been improved, these explainable recommendation models still have some limitations. First of all, most of these explainable recommendation models do not take the dynamics o...

AI Technical Summary

Problems solved by technology

Secondly, the comment-level and feature-level explanations provided by these interpretable recommendation models have limitations. The comment-level explanation is to provide the entire comment of other users to the current user as an explanation. Since user comments contain a lot of personal preference information , so that the explanation at the comment level is too redundant, and the explanation at the feature level is to provide an item feature that the current user is concerned about as an explanation. Because a feature word contains too little information, the explanation at the feature level is too concise, which may can confuse users

Finally, although these recommendation models all use user reviews as input to the model, none of these recommendation models deeply mine the rich semantic information contained in reviews.

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