Fuzzy prototype transfer learning for non-overlapping cross-domain recommendation

Abstract

Cross-domain recommendation (CDR) offers an efficient and effective solution to mitigate data sparsity in recommender systems. Existing research primarily focuses on exploring knowledge transfer based on overlapping entities or auxiliary contents between domains. However, there is little research on the real non-overlapping cross-domain recommendation (NCDR) problems, even though it poses a more general and applicable prospect. The core challenge of NCDR lies in the difficulty of finding the correct and useful knowledge transfer bridge between domains without relying on the explicit overlapping identities. Utilizing the inherent similarity and fuzzy characteristics of users and items in the latent feature space, this paper investigates a Fuzzy Prototype Transfer (FPT) learning method for the NCDR problem. FPT jointly optimizes prototypes and individual features for both users and items in target domain under the guidance of source features. An end-to-end learnable fuzzy clustering module based on maximum entropy regularization is proposed to learn both user and item fuzzy clustering assignments and fuzzy fusion prototypes. Lastly, by constructing an asymmetric dual-prototype fuzzy transfer module, similar user and item features across domains are found and aligned effectively. Extensive experiments demonstrate FPT’s superior performance over the state-of-the-art methods while maintaining lower inference and memory costs than those of the baselines.