A trust-aware latent space mapping approach for cross-domain recommendation

Cross-domain recommendation is becoming increasingly popular recently. Existing cross-domain recommendation often assumes that, a sufficient set of bridged users across domains is given in advance which disregards the scenario with insufficient bridged users. In this paper, we propose a novel Trust-aware Latent Space Mapping approach for Cross-domain Recommendation, called TLSM-CDR. This represents one of the first attempts to address the challenge of insufficient bridged users from the perspective of users' trust relationships to facilitate user sharing cross-domain recommendation. First, our model employs the Probabilistic Matrix Factorization (PMF) to generate user and item matrices. Then, Deep Neural Network (DNN) and graph Laplacian are seamlessly incorporated into our trust-aware nonlinear mapping function to capture the latent space relationships between both bridged and non-bridged users. Finally, we predict the optimized users' rating matrix in the target domain. Extensive experiments conducted on two real-world datasets demonstrate that, our TLSM-CDR model significantly outperforms several state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel Trust-aware Latent Space Mapping approach (TLSM-CDR) for Cross-domain Recommendation, addressing the challenge of insufficient bridged users by considering users' trust relationships. Experimental results demonstrate that the TLSM-CDR model significantly outperforms several state-of-the-art methods on two real-world datasets.