Learn More
A prominent approach in collaborative filtering based recommender systems is using dimensionality reduction (matrix factorization) techniques to map users and items into low-dimensional vectors. In such systems, a higher inner product between a user vector and an item vector indicates that the item better suits the user's preference. Traditionally,(More)
We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the ``cold start'' problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization. However, traditional(More)
  • 1