A. Mnih; JMLR W&CP 18:169–181, 2012.
Taxonomy-Informed Latent Factor Models
for Implicit Feedback
We describe a latent-factor-model-based approach to the Track 2 task of KDD Cup
2011, which required learning to discriminate between highly rated and unrated items from a
large dataset of music ratings. We take the pairwise ranking route, training our models to
rank the highly rated items above the unrated items that are sampled from the same
distribution. Using the item relationship information from the provided taxonomy to
constrain item representations results in improved predictive performance. Providing the
model with features summarizing the user’s rating history as it relates to the item
being ranked leads to further gains, producing the best single model result on Track
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