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cs.IR2021
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation
Seongwon Jang, Hoyeop Lee, Hyunsouk Cho +1
Sequential recommendation techniques provide users with product recommendations fitting their current preferences by handling dynamic user preferences over time. Previous studies h…
cs.IR2021
Freudian and Newtonian Recurrent Cell for Sequential Recommendation
Hoyeop Lee, Jinbae Im, Chang Ouk Kim +1
A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural langu…
cs.IR2019
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
Hoyeop Lee, Jinbae Im, Seongwon Jang +2
This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preferen…