A Deep Learning Model for Cross-Domain Serendipity Recommendations

Abstract

Serendipity means unexpected discoveries that are valuable, with positive outcomes ranging from personal benefits to scientific breakthroughs. This study proposes a cross-domain recommendation model, called SerenCDR , to model serendipity. SerenCDR leverages the knowledge beyond one domain as well as mitigates the inherent data sparsity problem in serendipity recommendations. The novelty of SerenCDR lies in the fact that it is the first deep learning-based cross-domain model for a serendipity task. More importantly, it does not rely on any overlapping users or overlapping items across different domains, which especially fits for the task of recommending serendipity, because serendipity in a single domain tends to be sparse; finding overlapping users or overlapping items in other domains are nearly impossible. To train and test SerenCDR , we have collected a two-domain ground truth dataset on serendipity, called SerenCDRLens . In addition, since we found that serendipity is sparse in SerenCDRLens , we designed an auxiliary loss function to supplement the main loss function to enhance serendipity learning. Through a series of experiments, we have harvested positive performance in recommending serendipity, empowering users with increased chances of bumping into unexpected but valuable discoveries.