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pro vyhledávání: '"Chung, Sehee"'
The common research goal of self-supervised learning is to extract a general representation which an arbitrary downstream task would benefit from. In this work, we investigate music audio representation learned from different contrastive self-supervi
Externí odkaz:
http://arxiv.org/abs/2207.04471
Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related
Externí odkaz:
http://arxiv.org/abs/2105.13135
Sequential recommendation techniques provide users with product recommendations fitting their current preferences by handling dynamic user preferences over time. Previous studies have focused on modeling sequential dynamics without much regard to whi
Externí odkaz:
http://arxiv.org/abs/2105.10868
A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed it
Externí odkaz:
http://arxiv.org/abs/2102.07645
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 preference in the cold state, existing recommender systems, such as Netflix, i
Externí odkaz:
http://arxiv.org/abs/1908.00413
Akademický článek
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Akademický článek
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Autor:
Wu, Cathy H., Nikolskaya, Anastasia, Huang, Hongzhan, Yeh, Lai‐Su L., Natale, Darren A., Vinayaka, C. R., Hu, Zhang‐Zhi, Mazumder, Raja, Kumar, Sandeep, Kourtesis, Panagiotis, Ledley, Robert S., Suzek, Baris E., Arminski, Leslie, Chen, Yongxing, Zhang, Jian, Cardenas, Jorge Louie, Chung, Sehee, Castro‐Alvear, Jorge, Dinkov, Georgi, Barker, Winona C.
Publikováno v:
Nucleic Acids Research; Jan2004, Vol. 32 Issue suppl 1, pd112-d114, 1p
