Generalizing matrix factorization through flexible regression priors

  title={Generalizing matrix factorization through flexible regression priors},
  author={Liang Zhang and Deepak Agarwal and Bee-Chung Chen},
Predicting user "ratings" on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work [4][32] have generalized matrix factorization to include both user and item… CONTINUE READING
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