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

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 rst 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 dierent 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.