Papers › Local Popularity and Time in top-N Recommendation

Local Popularity and Time in top-N Recommendation

11 Jul 2018arXiv:1807.04204archive 2025-07-28

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.

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sisinflab/DatasetsSplits officialmentioned in papermentioned on GitHub report
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Collaborative FilteringRecommendation Systems

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