Factorization meets the item embedding

Dawen Liang, Jaan Altosaar, Laurent Charlin, David Blei

Abstract:

Matrix factorization (MF) models and their extensions are
standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user
and item latent factors. In this paper, we propose a cofactorization model, CoFactor, which jointly decomposes the
user-item interaction matrix and the item-item co-occurrence
matrix with shared item latent factors. For each pair of items,
the co-occurrence matrix encodes the number of users that
have consumed both items. CoFactor is inspired by the recent
success of word embedding models (e.g., word2vec) which
can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the
performance over MF models on several datasets with little
additional computational overhead. We provide qualitative
results that explain how CoFactor improves the quality of
the inferred factors and characterize the circumstances where
it provides the most significant improvements.