Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility
S Y Liao, Yujuan Ding, P.Y. Mok · 2023
Fashion recommendation considers both product similarity and compatibility, and has drawn increasing research interest. It is a challenging task because it often needs to use information from different sources, such as visual content or textual descriptions for the prediction of user preferences. In terms of complementary recommendation, existing approaches were dedicated to modeling either product compatibility or users’ personalization in a direct and decoupled manner, yet overlooked additional relations hidden within historical user-product interactions. In this paper, we propose a Normalized indirect Personal Compatibility modeling scheme based on Bayesian Personalized Ranking (NiPC-BPR) for mix-and-match clothing recommendations. We exploit direct and indirect personalization and compatibility relations from the user and product interactions, and effectively integrate various multi-modal data. Extensive experimental results on two benchmark datasets show that our method outperforms other methods by large margins.