Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems

Collaborative filtering is one of the most promising used techniques in recommender systems to provide personalized recommendations. However, it suffers from the data sparsity problem due to insufficient user preference data. The promising solution to this problem is knowledge exploitation from other related source domains through transfer learning mechanism. Although several works have been proposed to enhance prediction performance of the target domain by leveraging the knowledge of source domains, extracting domain-independent knowledge from source domains and preventing the negative knowledge both are challenging and open problems for cross-domain recommender systems framework. In this paper, we propose a method namely Knowledge Transfer by Domain-independent User Latent Factor for Cross-domain Recommender Systems. The propose method uses matrix tr-factorization by which a rating matrix decomposes into three sub-matrices. Advantage of tri-factorization is to be able to prevent domain effect knowledge transfer. This allows to enhance the overall performance of prediction in the target domain. We collectively factorize both domain rating matrices by matrix tri-factorization with the constraint of shared user latent factor across domains. The constraint holds to be true due to users fully overlap across domains. The loss functions of matrix tri-factorization are combined with a tuning parameter that is able to prevent negative transferring of a source domain. The combined loss function is optimized by stochastic gradient descent algorithm. The superiority of our proposed method over existing state-of-the-art methods is demonstrated by experimental results which have been done on real-world Amazon co-purchasing metadata. (C) 2020 Elsevier B.V. All rights reserved.