Multiple Objective Explainable Recommendation Based on Knowledge Graph

doi:10.3969/j.issn.1006-2475.2024.03.006

Abstract

Abstract: Abstract： Most of the existing recommendation system research focuses on how to improve the accuracy of recommendation， but neglects the explainability of recommendation. In order to maximize the satisfaction with recommendation items of users， a multi-objective explainable recommendation model based on knowledge graph is proposed to optimize the accuracy， novelty， diversity and explainability of recommendations. Firstly， the explainable candidate list of users is obtained by knowledge graph， and the explainable candidate list is quantified by using a unified method based on the path between the interaction item and the recommendation item of target users. Finally， the explainable candidate list is optimized by multi-objective optimization algorithm， and the final recommendation list is obtained. The experimental results on the dataset of Movielens and Epinions show that the proposed model can improve the explainability of recommendations without compromising accuracy， novelty， and diversity.

Key words: Key words： knowledge graph, recommendation system, explainability, multi-objective optimization

CLC Number:

TP391

YANG Meng, YANG Jin, CHEN Bu-qian. Multiple Objective Explainable Recommendation Based on Knowledge Graph[J]. Computer and Modernization, 2024, 0(03): 34-40.

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