Maja Rudolph, San Gultekin, Paisley John, Shih-Fu Chang. Probabilistic Canonical Tensor Decomposition for Predicting User Preference. In Personalization: Methods and Applications Workshop, Conference on Neural Information Processing Systems, 2014.
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We propose a model to infer a user's preference ranking over musicians from a sparse set of pairwise preferences of the form ``user k prefers artist i over artist j''. The goal is to approximate the data with a low-rank factor model using canonical tensor decomposition. A user-specific pairwise preference is modelled as the sign of a 3-way tensor inner product of latent factor vectors associated with the user and the two musicians being compared. The latent factors are learned using mean-field variational inference and can be used to predict all missing preference pairs. We validate our approach on a real data set of 80M pairwise preferences aggregated from the interaction of 200K users with an online radio
@InProceedings{rudolph2014probabilistic,
Author = {Rudolph, Maja and Gultekin, San and John, Paisley and Chang, Shih-Fu},
Title = {Probabilistic Canonical Tensor Decomposition for Predicting User Preference},
BookTitle = {Personalization: Methods and Applications Workshop, Conference on Neural Information Processing Systems},
Year = {2014}
}
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