Selective Knowledge Transfer for Cross-Domain Collaborative Recommendation

doi:10.1109/ACCESS.2021.3061279

2021-01-01

Hongwei Zhang

Xiangwei Kong *

Yujia Zhang

DOI：10.1109/ACCESS.2021.3061279

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摘要

En 中文

Data sparsity is a major challenge for collaborative filtering recommender systems. A promising solution is to utilize feedback or ratings from multiple domains to improve the performance of recommendations in a collective way, known as the cross-domain recommendation. Cross-domain recommendation using heterogeneous feedback is a popular solution, which transfers knowledge from the more easily available auxiliary binary feedback to improve the prediction performance of the target domain. Most of the existing work focuses on the transfer of knowledge between different domains from the same website, where user behavior data in different domains can be fully shared. The existing work mainly assumes that data from different domains can be fully shared. However, due to the constraints of business privacy policies, it is difficult to directly share exactly the same user behavior data between different e-commerce websites. It results in that the user's latent factors learned in the auxiliary domain cannot be directly transferred to the target domain, otherwise, it will cause a negative transfer issue. In this article, we consider that the auxiliary domain with numerical ratings and target domains with binary feedbacks only share overlapping items rather than users. We propose a Selective Knowledge Transfer for Cross-domain Collaborative Recommendation, called SKT. The proposed SKT framework not only transfers the item's latent factors learned from the auxiliary domain to the target domain, but also selectively transfers the user's latent factors learned from the auxiliary domain to the target domain. In addition, due to the introduction of co-graph regularization of user graphs and item graphs, SKT can maintain respective intrinsic geometric structure within each domain and thus avoid negative transfer issue. Extensive experiments conducted on two real-world datasets show that our SKT method is significantly better than all baseline methods at various density levels.

Keyword:

Knowledge transfer

Sparse matrices

Collaboration

Bridges

Transfer learning

Videos

Task analysis

Transfer learning (TL)

cross-domain recommendation

selective knowledge transfer

sparsity