IR-Rec: An interpretive rules-guided recommendation over knowledge graph

Most existing recommendation methods focus on the improvement of recommender accu-racy while ignoring the influence of recommended explanation. Recommender explainabil-ity is an efficient way to help consumers make much more suitable decisions and enhance their acceptance and trustfulness in recommender systems (RSs). Incorporating a knowl-edge graph (KG) into RS is a promising way to improve recommender results while enhanc-ing the strength of explanation. In this paper, an interactive rules-guided recommender (IR-Rec) framework based on KG is proposed. First, an existing KG is enriched by introduc-ing it to facts about e-commerce. Then, a number of paths are extracted from the enhanced KG for user-item interactions that are able to ascertain the underlying reason for recom-mendations according to the semantic strength of the KG. These paths are summarized into some common behavior rules that have the ability to explain the underlying motivations of users. According to the characteristics of users, items, and rules, different neural networks are designed, such as a graph convolutional network, to learn more accurate embeddings. Furthermore, recommendations that meet users' personalized interests are made by com-bining the public behavior rules and their individual features. Extensive experiments are carried out on four Amazon datasets for top -K recommendation. All the results show that the proposed method performs better than other respective baselines and demonstrate the effectiveness of rule extraction for making recommendations. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study introduces an interactive rules-guided recommender framework based on knowledge graph, enriching the content of knowledge graph and extracting paths and behavior rules to achieve better recommendation results.