Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering Export

@inproceedings{citeulike:5070640, abstract = {One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, classes are highly imbalanced, and the vast majority of data points are missing. The idea of introducing weights for missing parts of a matrix has recently been shown to help in OCCF. While existing weighting approaches mitigate the first two problems above, a sparsity preserving solution that would allow to efficiently utilize data sets with e.g., hundred thousands of users and items has not yet been reported. In this paper, we study three different collaborative filtering frameworks: Low-rank matrix approximation, probabilistic latent semantic analysis, and maximum-margin matrix factorization. We propose two novel algorithms for large-scale OCCF that allow to weight the unknowns. Our experimental results demonstrate their effectiveness and efficiency on different problems, including the Netflix Prize data.}, address = {New York, NY, USA}, author = {Pan, Rong and Scholz, Martin}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5070640}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557094}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557094}, doi = {10.1145/1557019.1557094}, isbn = {978-1-60558-495-9}, keywords = {collaborative-filtering, sparsity, uncertainty, weighting}, location = {Paris, France}, pages = {667--676}, posted-at = {2009-10-09 21:33:59}, priority = {5}, publisher = {ACM}, title = {Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering}, url = {http://dx.doi.org/10.1145/1557019.1557094}, year = {2009} }

TY - CONF ID - citeulike:5070640 L3 - citeulike-article-id:5070640 N2 - One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, classes are highly imbalanced, and the vast majority of data points are missing. The idea of introducing weights for missing parts of a matrix has recently been shown to help in OCCF. While existing weighting approaches mitigate the first two problems above, a sparsity preserving solution that would allow to efficiently utilize data sets with e.g., hundred thousands of users and items has not yet been reported. In this paper, we study three different collaborative filtering frameworks: Low-rank matrix approximation, probabilistic latent semantic analysis, and maximum-margin matrix factorization. We propose two novel algorithms for large-scale OCCF that allow to weight the unknowns. Our experimental results demonstrate their effectiveness and efficiency on different problems, including the Netflix Prize data. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 676 TI - Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 667 KW - collaborative-filtering KW - sparsity KW - uncertainty KW - weighting AU - Pan, Rong AU - Scholz, Martin PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557094 ER -