Sequential Recommender via Time-aware Attentive Memory Network

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 faces several challenges: (1) Behaviors are much more complex than words in sentences, so traditional attentive and recurrent models may fail in capturing the temporal dynamics of user preferences... (read more)

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METHOD TYPE
Memory Network
Working Memory Models
GRU
Recurrent Neural Networks