Explainable Matrix Factorization for Collaborative Filtering

  title={Explainable Matrix Factorization for Collaborative Filtering},
  author={Behnoush Abdollahi and Olfa Nasraoui},
Explanations have been shown to increase the user’s trust in recommendations in addition to providing other benefits such as scrutability, which is the ability to verify the validity of recommendations. Most explanation methods are designed for classical neighborhood-based Collaborative Filtering (CF) or rule-based methods. For the state of the art Matrix Factorization (MF) recommender systems, recent explanation methods, require an additional data source, such as item content data, in addition… CONTINUE READING

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