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2009
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Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering

9 years 9 months ago
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
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 ...
Rong Pan, Martin Scholz
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where KDD
Authors Rong Pan, Martin Scholz
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