Sequential Recommender via Time-aware Attentive Memory Network
Wendi Ji, Keqiang Wang, Xiaoling Wang, Tingwei Chen, Alexandra I. Cristea
Abstract
Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still face several challenges: (1) Behaviors are much more com- plex than words in sentences, so traditional attentive and recurrent models have limitations capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent.
Topics & Concepts
Computer scienceRecommender systemLong short term memoryTerm (time)Artificial intelligenceFace (sociological concept)Deep learningDynamics (music)Machine learningRecurrent neural networkArtificial neural networkPsychologyPedagogySociologyPhysicsQuantum mechanicsSocial scienceRecommender Systems and TechniquesTopic ModelingAdvanced Graph Neural Networks