Dynamic Embeddings for Interaction Prediction
Zekarias T. Kefato, Šarūnas Girdzijauskas, Nasrullah Sheikh, Alberto Montresor · 2021
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identifying relevant items that match user preferences, there is still a range of aspects that could be considered to further improve their performance. For example, often RSs are centered around the user, who is modeled using her recent sequence of activities. Recent studies, however, have shown the effectiveness of modeling the mutual interactions between users and items using separate user and item embeddings.