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- W3094595891 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." @default.
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- W3094595891 date "2020-10-19" @default.
- W3094595891 modified "2023-09-23" @default.
- W3094595891 title "Sequential Recommender via Time-aware Attentive Memory Network" @default.
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- W3094595891 doi "https://doi.org/10.1145/3340531.3411869" @default.
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