Tag-aware recommender systems by fusion of collaborative filtering algorithms Export

@inproceedings{citeulike:2747493, abstract = {Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.}, address = {New York, NY, USA}, author = {Tso Sutter, Karen H. L. and Marinho, Leandro B. and Thieme, Lars S.}, booktitle = {SAC '08: Proceedings of the 2008 ACM symposium on Applied computing}, citeulike-article-id = {2747493}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1363686.1364171}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1363686.1364171}, doi = {10.1145/1363686.1364171}, isbn = {978-1-59593-753-7}, keywords = {folksonomy, recommendation, tag}, location = {Fortaleza, Ceara, Brazil}, pages = {1995--1999}, posted-at = {2009-02-07 06:07:46}, priority = {2}, publisher = {ACM}, title = {Tag-aware recommender systems by fusion of collaborative filtering algorithms}, url = {http://dx.doi.org/10.1145/1363686.1364171}, year = {2008} }

TY - CONF ID - citeulike:2747493 L3 - citeulike-article-id:2747493 N2 - Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results. CY - Fortaleza, Ceara, Brazil SN - 978-1-59593-753-7 AD - New York, NY, USA EP - 1999 TI - Tag-aware recommender systems by fusion of collaborative filtering algorithms PB - ACM T2 - SAC '08: Proceedings of the 2008 ACM symposium on Applied computing SP - 1995 KW - folksonomy KW - recommendation KW - tag AU - Tso Sutter, Karen AU - Marinho, Leandro AU - Thieme, Lars PY - 2008/// UR - http://dx.doi.org/10.1145/1363686.1364171 ER -