%0 Conference Paper %1 Huang:2005:LPA:1065385.1065415 %A Huang, Zan %A Li, Xin %A Chen, Hsinchun %B Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries %C New York, NY, USA %D 2005 %I ACM %K collaborative link linkrecommender prediction %P 141--142 %R 10.1145/1065385.1065415 %T Link prediction approach to collaborative filtering %U http://doi.acm.org/10.1145/1065385.1065415 %X <i>Recommender systems</i> can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is <i>collaborative filtering</i>, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms. %@ 1-58113-876-8

@inproceedings{Huang:2005:LPA:1065385.1065415, abstract = {<i>Recommender systems</i> can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is <i>collaborative filtering</i>, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.}, acmid = {1065415}, added-at = {2013-02-19T15:25:04.000+0100}, address = {New York, NY, USA}, author = {Huang, Zan and Li, Xin and Chen, Hsinchun}, biburl = {https://www.bibsonomy.org/bibtex/29e85f812e9b1d7c4c1be21c1b9ecd187/samthomas}, booktitle = {Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries}, description = {Link prediction approach to collaborative filtering}, doi = {10.1145/1065385.1065415}, interhash = {c8b7b51c11aa5d73d1f05dc2f4178cac}, intrahash = {9e85f812e9b1d7c4c1be21c1b9ecd187}, isbn = {1-58113-876-8}, keywords = {collaborative link linkrecommender prediction}, location = {Denver, CO, USA}, numpages = {2}, pages = {141--142}, publisher = {ACM}, series = {JCDL '05}, timestamp = {2013-02-19T15:28:06.000+0100}, title = {Link prediction approach to collaborative filtering}, url = {http://doi.acm.org/10.1145/1065385.1065415}, year = 2005 }