Heterogeneous and clustering-enhanced personalized preference transfer for cross-domain recommendation

As a promising solution to alleviate the critical cold-start problem in recommendation systems, Cross-Domain Recommendation (CDR) aims to transfer users' preferences from the source domain to the target domain. However, the negligence of semantic differences of heterogeneous relations when modeling user preferences and the non-consideration of a user's common characteristics inferred from similar users when transferring the user's preferences make recent CDR works cannot well handle the two core issues of CDR-'what to transfer' and 'how to transfer'. To this end, we propose a novel heterogeneous and clustering-enhanced user preference transfer model for CDR (named HCCDR). To well address the first issue, the heterogeneous latent factor modeling component is firstly built to compute high-quality representations of users and items to be transferred based on the heterogeneous relations among users and items. Note that the heterogeneous relations with different semantics are processed with different models. Then, the clustering-enhanced preference transfer component well addresses the second issue by learning an effective personalized preference transfer function between two domains for a user, where the individual and common characteristics of the user are concurrently considered. Finally, the personalized recommendation component achieves the personalized recommendation in the target domain based on the transferred user embeddings calculated via the learned personalized preference transfer functions. Experimental results performed on large public datasets demonstrate that the proposed HCCDR markedly outperforms all baselines. In particular, HCCDR gains a maximum performance improvement of 12.69% (or 8.99%) for RMSE (or MAE) compared with the best baseline.