Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Jibang Wu, Renqin Cai, Hongning Wang · 2020
Predicting users’ preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction.