%0 Conference Paper %1 Sahebi:2015:TTT:2792838.2800188 %A Sahebi, Shaghayegh %A Brusilovsky, Peter %B Proceedings of the 9th ACM Conference on Recommender Systems %C New York, NY, USA %D 2015 %I ACM %K cross-domain myown recommender %P 131--138 %R 10.1145/2792838.2800188 %T It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering %U http://doi.acm.org/10.1145/2792838.2800188 %X As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations. %@ 978-1-4503-3692-5

@inproceedings{Sahebi:2015:TTT:2792838.2800188, abstract = {As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.}, acmid = {2800188}, added-at = {2016-03-23T02:39:51.000+0100}, address = {New York, NY, USA}, author = {Sahebi, Shaghayegh and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2d11baf64e2f55ec57d037dfcb6c86bb0/brusilovsky}, booktitle = {Proceedings of the 9th ACM Conference on Recommender Systems}, description = {It Takes Two to Tango}, doi = {10.1145/2792838.2800188}, interhash = {9d5b5cf77dd14b07c6062ece1e1b4ae9}, intrahash = {d11baf64e2f55ec57d037dfcb6c86bb0}, isbn = {978-1-4503-3692-5}, keywords = {cross-domain myown recommender}, location = {Vienna, Austria}, numpages = {8}, pages = {131--138}, publisher = {ACM}, series = {RecSys '15}, timestamp = {2016-03-23T02:39:51.000+0100}, title = {It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering}, url = {http://doi.acm.org/10.1145/2792838.2800188}, year = 2015 }