FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems

@inproceedings{Li2017FEXIPROFA,
  title={FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems},
  author={Hui Li and Tsz Nam Chan and Man Lung Yiu and Nikos Mamoulis},
  booktitle={SIGMOD Conference},
  year={2017}
}
Recommender systems have many successful applications in e-commerce and social media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most popular recommendation approaches; the original user-product rating matrix R with millions of rows and columns is decomposed into a user matrix Q and an item matrix P, such that the product QT P approximates R. Each column q (p) of Q (P) holds the latent factors of the corresponding user (item), and qT p is a prediction of the… CONTINUE READING