Towards Self-Explaining Sequence-Aware Recommendation

Self-explaining models are becoming an important perk of recommender systems, as they help users understand the reason behind certain recommendations, which encourages them to interact more often with the platform. In order to personalize recommendations, modern approaches make the model aware of the user behavior history for interest evolution representation. However, existing explainable recommender systems do not consider the past user history to further personalize the explanation based on the user interest fluctuation. In this work, we propose a SEQuence-Aware Explainable Recommendation model (SEQUER) that is able to leverage the sequence of user-item review interactions to generate better explanations while maintaining recommendation performance. Experiments validate the effectiveness of our proposal on multiple recommendation scenarios. Our source code and preprocessed datasets are available at https://github.com/alarca94/sequer-recsys23.