Corpus ID: 219530477

Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation

@article{Boratto2020ConnectingUA,
  title={Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation},
  author={Ludovico Boratto and Gianni Fenu and Mirko Marras},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.04275}
}

Ludovico Boratto, Gianni Fenu, Mirko Marras

Published 2020

Computer Science

ArXiv

Recommender systems learn from historical data that is often non-uniformly distributed across items, so they may end up suggesting popular items more than niche items. This can hamper user interest and several qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the platform. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages… CONTINUE READING

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