Hybrid algorithms for recommending new items

@inproceedings{Cremonesi2011HybridAF,
  title={Hybrid algorithms for recommending new items},
  author={Paolo Cremonesi and Roberto Turrin and Fabio Airoldi},
  booktitle={HetRec '11},
  year={2011}
}
Despite recommender systems based on collaborative filtering typically outperform content-based systems in terms of recommendation quality, they suffer from the new item problem, i.e., they are not able to recommend items that have few or no ratings. This problem is particularly acute in TV applications, where the catalog of available items (e.g., TV programs) is very dynamic. On the contrary, content-based recommender systems are able to recommend both old and new items but the general quality… CONTINUE READING
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