Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.
Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback
