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題名:	A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression
作者:	沈錳坤 Li, Cheng-Te;Hsu, Chia-Tai;Shan, Man-Kwan
貢獻者:	資科系
日期:	2018-11
上傳時間:	2019-01-24 11:28:04 (UTC+8)
摘要:	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.
關聯:	ACM Transactions on Intelligent Systems and Technology, Volume 9 Issue 6, Article No. 67
資料類型:	article
DOI 連結:	https://doi.org/10.1145/3231601
DOI:	10.1145/3231601
顯示於類別:	[資訊科學系] 期刊論文

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