A cross-domain recommendation mechanism for cold-start users based on partial least squares regression

Cheng Te Li, Chia Tai Hsu, Man Kwan Shan

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users' time is saved and sellers' profits are increased. Cross-domain recommender systems aim to recommend items based on users' different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains' rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory.

Original languageEnglish
Article number67
JournalACM Transactions on Intelligent Systems and Technology
Issue number6
Publication statusPublished - 2018 Nov

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence


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