Matrix factorization in Cross-domain Recommendations Framework by Shared Users Latent Factors

Abstract

Matrix factorization is one of the most commonly used method of collaborative filtering (CF) for generating personalized recommendations to users. A main limitation of CF is that It fully depends on observed ratings and may fail if these observed ratings are in limited amount called sparsity problem. Addressing this problem, cross-domain recommendations came into existence where transfer learning mechanism is applied to mitigate sparsity problem and increase performance of the target domain using other related source domains.In this paper, we propose the method for knowledge transfer from source domain to target domain through shared users latent factors. Firstly, we apply traditional matrix factorization (MF) method in source domain to learn latent factors of users and items through objective function of MF. After that, learned latent factors of users are directly transferred to target domain. Modify objective function of MF and learn users/items latent factors of the target domain. Finally, prediction on unobserved ratings in the target domain is made using inner product of respective user and item latent factors. Experimental results demonstrate that our proposed method substantially work well from other without and with transfer learning methods in terms of MAE and RMSE metrics.