Cross-domain recommendation based on latent factor alignment


Recently, various cross-domain recommendation (CDR) models are proposed to overcome the sparsity problem, which leverage relatively abundant rating data from the auxiliary domain to improve recommendation performance of target domain. Though matrix factorization-based collaborative filtering algorithms gain dominance in single-domain recommendation systems, they cannot be used directly in cross-domain cases as the obtained latent factors of the target and auxiliary domains may not be aligned, which will lead to inaccurate knowledge transfer from the auxiliary domain to the target one. A CDR model named CDCFLFA is presented in this paper to solve this problem. In CDCFLFA, firstly latent factors between the two domains are aligned based on pattern matching. Then, user preferences of the auxiliary domain are transferred to update the original user latent vectors of target domain. Finally, a linear least square problem is solved to compute the item latent vectors of target domain and thus unknown ratings are obtained according to the updated user and item latent vectors. CDCFLFA does not require the same user or item sets between the two domains. Extensive experiments are conducted, and the results show that CDCFLFA achieves smaller MAE and RMSE values and larger precision and recall than the previous single- and cross-domain recommendation methods. Hence, CDCFLFA can be regarded as an effective cross-domain extension of single-domain matrix factorization algorithm.

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This work is jointly sponsored by National Natural Science Foundation of China (Nos. 61402246, 61273180, 61702295, 61973180), Natural Science Foundation of Shandong Province (Nos. ZR2019MF014, ZR2019MF033), and key research and development program of Shandong Province (No. 2018GGX101052).

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Yu, X., Hu, Q., Li, H. et al. Cross-domain recommendation based on latent factor alignment. Neural Comput & Applic (2021).

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  • Cross-domain recommendation
  • Latent factor alignment
  • Linear least square problem
  • Transfer learning