Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

TL;DR

This paper introduces TMER-RL, a reinforcement learning approach that models dynamic user-item interactions on evolving knowledge graphs to improve the accuracy and explainability of recommendations.

Contribution

It proposes a novel reinforcement learning framework with attention mechanisms for path-based, dynamic, and explainable recommendations using knowledge graphs.

Findings

Achieves state-of-the-art performance on real-world datasets.

Effectively models dynamic user-item evolutions.

Outperforms existing static and sequential recommendation methods.

Abstract

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.