Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

@article{Liang2016FactorizationMT,
  title={Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence},
  author={Dawen Liang and Jaan Altosaar and Laurent Charlin and David M. Blei},
  journal={Proceedings of the 10th ACM Conference on Recommender Systems},
  year={2016}
}
Matrix factorization (MF) models and their extensions are standard in modern recommender systems. [] Key Method 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…
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