Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Jibang Wu, Renqin Cai, Hongning Wang
Abstract
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.
Topics & Concepts
Recommender systemComputer scienceDéjà vuFocus (optics)Context (archaeology)Mechanism (biology)Event (particle physics)Artificial intelligenceMachine learningData scienceCognitive psychologyPsychologyHistoryEpistemologyQuantum mechanicsPhilosophyOpticsPhysicsArchaeologyRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchTopic Modeling