A hinge-loss based codebook transfer for cross-domain recommendation with non-overlapping data
作者：
Highlights：
• We propose a model for cross-domain recommendation to address the data sparsity.
• The proposed method is useful when the domains do not share common users or items.
• Co-clustering technique is used to construct the codebook from the source domain.
• Learnt codebook gets transferred to the target domain in a novel way via hinge loss.
• We validate the performance of the proposed method on benchmark datasets.
摘要
•We propose a model for cross-domain recommendation to address the data sparsity.•The proposed method is useful when the domains do not share common users or items.•Co-clustering technique is used to construct the codebook from the source domain.•Learnt codebook gets transferred to the target domain in a novel way via hinge loss.•We validate the performance of the proposed method on benchmark datasets.
论文关键词：Collaborative filtering,Matrix factorisation,Codebook,Transfer learning,Cross-domain recommender systems
论文评审过程：Received 25 August 2021, Revised 14 December 2021, Accepted 7 February 2022, Available online 8 February 2022, Version of Record 16 February 2022.
论文官网地址：https://doi.org/10.1016/j.is.2022.102002
