1. NCCU Academic Hub
  2. 學術產出
  3. 資訊學院
  4. 期刊論文
  5. Item

學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

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 24-一月-2019 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 https://doi.org/10.1145/3231601
dc.contributor 資科系
dc.creator (作者) 沈錳坤
dc.creator (作者) Li, Cheng-Te;Hsu, Chia-Tai;Shan, Man-Kwan
dc.date (日期) 2018-11
dc.date.accessioned 24-一月-2019 11:28:04 (UTC+8)-
dc.date.available 24-一月-2019 11:28:04 (UTC+8)-
dc.date.issued (上傳時間) 24-一月-2019 11:28:04 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122153-
dc.description.abstract (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.
dc.format.extent 3797253 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) ACM Transactions on Intelligent Systems and Technology, Volume 9 Issue 6, Article No. 67
dc.title (題名) A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1145/3231601
dc.doi.uri (DOI) https://doi.org/10.1145/3231601