Beyond Recommendations: Sequential Recommendation with Collaborative Explanation

Explainability is an essential challenge in recommender systems and has gained significant attention recently. Learning to rank explanations for recommendations allows for providing top-ranked justifications alongside recommended items, benefiting from a unified modeling process and the use of standard evaluation metrics. However, existing approaches face two primary limitations. First, adding an explanation facet intensifies data sparsity, making traditional tensor reconstruction objective less effective. Second, a discrepancy exists between the optimization of explanations during training and the goal of providing interaction-based explanations at inference. In this work, we propose Sequential recommendation with Collaborative Explanation (SCE), a novel framework that models sequential user patterns with a specially designed learning objective to address data sparsity and better align recommendation with explanation goals. To enhance the factual accuracy of ranked explanations, we integrate attribute information as external knowledge into the explanations. Our SCE framework offers superior model-agnostic flexibility, seamlessly supporting arbitrary sequential models such as GRU4Rec, SASRec, and others, to deliver accurate recommendations and associated explanations. By integrating mutual information and attribute enhancement, our approach achieves significant improvements in both recommendation and explanation performance. Our extensive experiments on three real-world datasets from various platforms demonstrate the effectiveness of our approach, outperforming state-of-the-art methods by a substantial margin.