Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality

Numerous Knowledge Graphs (KGs) are being created to make recommender systems not only intelligent but also knowledgeable. Reinforcement recommendation reasoning is a recent approach able to model high-order user-product relations, according to the KG. This type of approach makes it possible to extract reasoning paths between the recommended product and already experienced products. These paths can be in turn translated into textual explanations to be provided to the user for a given recommendation. However, none of the existing approaches has investigated user-level properties of a single or a group of reasoning paths. In this paper, we propose a series of quantitative properties that monitor the quality of the reasoning paths, based on recency, popularity, and diversity. We then combine in- and post-processing approaches to optimize for both recommendation quality and reasoning path quality. Experiments on three public data sets show that our approaches significantly increase reasoning path quality according to the proposed properties, while preserving recommendation quality. Source code, data sets, and KGs are available at https://tinyurl.com/bdbfzr4n.