Factorization meets the neighborhood: a multifaceted collaborative filtering model Export

@inproceedings{citeulike:3800901, abstract = {Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.}, address = {New York, NY, USA}, author = {Koren, Yehuda}, booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {3800901}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1401890.1401944}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1401890.1401944}, doi = {10.1145/1401890.1401944}, isbn = {978-1-60558-193-4}, keywords = {classification, facets, matrix}, location = {Las Vegas, Nevada, USA}, pages = {426--434}, posted-at = {2009-08-24 20:57:39}, priority = {2}, publisher = {ACM}, title = {Factorization meets the neighborhood: a multifaceted collaborative filtering model}, url = {http://dx.doi.org/10.1145/1401890.1401944}, year = {2008} }

TY - CONF ID - citeulike:3800901 L3 - citeulike-article-id:3800901 N2 - Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task. CY - Las Vegas, Nevada, USA SN - 978-1-60558-193-4 AD - New York, NY, USA EP - 434 TI - Factorization meets the neighborhood: a multifaceted collaborative filtering model PB - ACM T2 - KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 426 KW - classification KW - facets KW - matrix AU - Koren, Yehuda PY - 2008/// UR - http://dx.doi.org/10.1145/1401890.1401944 ER -