Directional and Explainable Serendipity Recommendation

Abstract

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users’ trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users’ long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.