Local Popularity and Time in top-N Recommendation
This work addresses recommendation accuracy for users by proposing an incremental improvement over existing popularity-based methods.
The paper tackles the problem of incorporating time-aware personalized popularity in recommender systems by considering items' popularity among neighbors and its temporal changes, showing a highly competitive accuracy compared to state-of-the-art model-based collaborative approaches.
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.