Local Popularity and Time in top-N Recommendation

TL;DR

This paper introduces a time-aware personalized popularity measure for recommender systems that considers local item popularity and its temporal dynamics, improving recommendation accuracy.

Contribution

It proposes a novel approach combining local popularity and temporal information, enhancing personalization without complex modeling.

Findings

Competitive accuracy compared to state-of-the-art models

Effective use of temporal popularity dynamics

Improved personalization in recommendations

Abstract

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.