Selective transfer learning for cross domain recommendation
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many real-world applications, preference data are usually sparse …
item preference data. In many real-world applications, preference data are usually sparse …
Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, W Xiang, W Pan… - arXiv preprint arXiv …, 2012 - arxiv.org
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many real-world applications, preference data are usually sparse …
item preference data. In many real-world applications, preference data are usually sparse …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - cse.ust.hk
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - scholar.archive.org
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - research.baidu.com
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …
Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, W Xiang, W Pan… - arXiv e …, 2012 - ui.adsabs.harvard.edu
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many real-world applications, preference data are usually sparse …
item preference data. In many real-world applications, preference data are usually sparse …
Selective transfer learning for cross domain recommendation
Z Lu - 2013 - repository.ust.hk
Collaborative Filtering (CF) aims to predict users' ratings on items according to historical
user-itempreference data. In many real-world applications, preference data are usually …
user-itempreference data. In many real-world applications, preference data are usually …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - cse.hkust.edu.hk
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - Citeseer
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …
[PDF][PDF] Selective Transfer Learning for Cross Domain Recommendation
Z Lu, E Zhong, L Zhao, EW Xiang, W Pan, Q Yang - cse.ust.hk
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many realworld applications, preference data are usually sparse …
item preference data. In many realworld applications, preference data are usually sparse …