Evaluating content novelty in recommender systems

Nicolás Torres
Nicolás Torres

Journal of Intelligent Information Systems, pp. 297-316, 2019.

Cited by: 2|Bibtex|Views62|DOI:https://doi.org/10.1007/s10844-019-00548-x
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Other Links: academic.microsoft.com|dblp.uni-trier.de|link.springer.com

Abstract:

Recommender systems are frequently evaluated using performance indexes based on variants and extensions of precision-like measures. As these measures are biased toward popular items, a list of recommendations simply must include a few popular items to perform well. To address the popularity bias challenge, new approaches for novelty and d...More

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