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| Field |
Value |
| Title: | A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression |
| Authors: | 沈錳坤
Li, Cheng-Te;Hsu, Chia-Tai;Shan, Man-Kwan |
| Contributors: | 資科系 |
| Date: | 2018-11 |
| Issue Date: | 2019-01-24 11:28:04 (UTC+8) |
| Abstract: | 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. |
| Relation: | ACM Transactions on Intelligent Systems and Technology, Volume 9 Issue 6, Article No. 67 |
| Data Type: | article |
| DOI: | https://doi.org/10.1145/3231601 |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 資科系 | |
| dc.creator (Authors) | 沈錳坤 | |
| dc.creator (Authors) | Li, Cheng-Te;Hsu, Chia-Tai;Shan, Man-Kwan | |
| dc.date (Date) | 2018-11 | |
| dc.date.accessioned | 2019-01-24 11:28:04 (UTC+8) | - |
| dc.date.available | 2019-01-24 11:28:04 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2019-01-24 11:28:04 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/122153 | - |
| dc.description.abstract (Abstract) | 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 (Relation) | ACM Transactions on Intelligent Systems and Technology, Volume 9 Issue 6, Article No. 67 | |
| dc.title (Title) | A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression | |
| dc.type (Data Type) | article | |
| dc.identifier.doi (DOI) | 10.1145/3231601 | |
| dc.doi.uri | https://doi.org/10.1145/3231601 | |
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