Supercharging recommender systems using taxonomies for learning user purchase behavior

PVLDB, pp. 956-967, 2012.

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Abstract:

Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the users and items into a smaller dimensional space, thereby clustering similar users and items together and subsequently compute similarity between unknown user-item ...More

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