%0 Conference Paper %1 conf/sac/QiCLHW12 %A Qi, Qi %A Chen, Zhenyu %A Liu, Jia %A Hui, Chengfeng %A Wu, Qing %B SAC %D 2012 %E Ossowski, Sascha %E Lecca, Paola %I ACM %K items recommendation tag %P 2008-2013 %T Using inferred tag ratings to improve user-based collaborative filtering. %U http://dblp.uni-trier.de/db/conf/sac/sac2012.html#QiCLHW12 %X User-based collaborative filtering is one of the most widely-used recommender methods. It recommends items to a user according to her similar users' opinions. The key point of user-based collaborative filtering is to compute users' similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations. With the rapid development of social tagging systems, social tagging data poses new opportunities for recommender systems. Many researchers have proposed different methods to exploit tagging data to improve the performance of recommender systems. In this paper, we propose a new approach to compute users' similarities using the inferred tag ratings. A user's preference for a tag t can be inferred based on her ratings of items tagged with t. A user rates too few such items, then her inferred rating to t may be inaccurate. Hence the relationships among tags are used to infer her preference for t based on all her item ratings, such that the preference of user could be accurate. Experiments were done on the MovieLens data set to evaluate the performance of our approach. The results show that our approach outperform traditional user-based collaborative filtering %@ 978-1-4503-0857-1

@inproceedings{conf/sac/QiCLHW12, abstract = {User-based collaborative filtering is one of the most widely-used recommender methods. It recommends items to a user according to her similar users' opinions. The key point of user-based collaborative filtering is to compute users' similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations. With the rapid development of social tagging systems, social tagging data poses new opportunities for recommender systems. Many researchers have proposed different methods to exploit tagging data to improve the performance of recommender systems. In this paper, we propose a new approach to compute users' similarities using the inferred tag ratings. A user's preference for a tag t can be inferred based on her ratings of items tagged with t. A user rates too few such items, then her inferred rating to t may be inaccurate. Hence the relationships among tags are used to infer her preference for t based on all her item ratings, such that the preference of user could be accurate. Experiments were done on the MovieLens data set to evaluate the performance of our approach. The results show that our approach outperform traditional user-based collaborative filtering}, added-at = {2014-03-01T12:22:38.000+0100}, author = {Qi, Qi and Chen, Zhenyu and Liu, Jia and Hui, Chengfeng and Wu, Qing}, biburl = {https://www.bibsonomy.org/bibtex/27b251e740a49a5864fc1a325ea1c59fc/inmantang}, booktitle = {SAC}, crossref = {conf/sac/2012}, editor = {Ossowski, Sascha and Lecca, Paola}, ee = {http://doi.acm.org/10.1145/2245276.2232110}, interhash = {cb3bfd9d371b0c75fab12e76a6ed9ceb}, intrahash = {7b251e740a49a5864fc1a325ea1c59fc}, isbn = {978-1-4503-0857-1}, keywords = {items recommendation tag}, pages = {2008-2013}, publisher = {ACM}, timestamp = {2014-03-01T12:22:38.000+0100}, title = {Using inferred tag ratings to improve user-based collaborative filtering.}, url = {http://dblp.uni-trier.de/db/conf/sac/sac2012.html#QiCLHW12}, year = 2012 }