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.
|Titolo:||Local popularity and time in top-N recommendation|
|Data di pubblicazione:||2019|
|Nome del convegno:||41st European Conference on IR Research, ECIR 2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-030-15712-8_63|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|