Nonlinear Latent Factorization by Embedding Multiple User Interests
Venue
ACM International Conference on Recommender Systems (RecSys) (2013)
Publication Year
2013
Authors
Jason Weston, Ron Weiss, Hector Yee
BibTeX
Abstract
Classical matrix factorization approaches to collaborative filtering learn a latent
vector for each user and each item, and recommendations are scored via the
similarity between two such vectors, which are of the same dimension. In this work,
we are motivated by the intuition that a user is a much more complicated entity
than any single item, and cannot be well described by the same representation.
Hence, the variety of a user’s interests could be better captured by a more complex
representation. We propose to model the user with a richer set of functions,
specifically via a set of latent vectors, where each vector captures one of the
user’s latent interests or tastes. The overall recommendation model is then
nonlinear where the matching score between a user and a given item is the maximum
matching score over each of the user’s latent interests with respect to the item’s
latent representation. We describe a simple, general and efficient algorithm for
learning such a model, and apply it to large scale, real world datasets from
YouTube and Google Music, where our approach outperforms existing techniques.
