Single-Target and Dual-Target Cross-Domain Recommendation

To address the data sparsity problem in recommender systems, cross-domain recommendation (CDR) has in recent years leveraged the relatively richer information from a richer (source) domain to only improve the recommendation performance in a sparser (target) domain with sparser information. Existing CDR approaches either directly replace a part of the latent representation of users/items in the sparser domain with the corresponding latent representation in the richer domain, or they map the latent representation of common users/items in the richer domain to fit those in the sparser domain. First, finding an accurate mapping of the latent factors across domains is crucial for enhancing recommendation accuracy for CDR. However, this is a challenging task because of the complex relationships that exist between the latent factors of the source and the target domains or systems. To this end, this thesis proposes a deep framework for both cross-domain and cross-system recommendations (DCDCSR) based on matrix factorisation (MF) models and a fully connected deep neural network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, this approach considers the rating sparsity degrees of individual users and items in different domains or systems and uses them to guide the DNN training process for utilising the rating data more effectively. Second, the existing CDR approaches are single-target approaches. However, each of the two domains may be relatively richer in certain types of information (e.g … Download paper here