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저자 양희윤, 김가형, 이지형 
논문지 Applied Sciences 
Vol. 12 
No.
pp. 4256-4272 
게재일 2022-04-22 
Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments.

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      Adversarial Detection with Gaussian Process Regression-based Detector
      2019.09.02
      저자: Sangheon Lee, Noo-ri Kim, Youngwha Cho, Jae-Young Choi, Suntae Kim, Jeong-Ah Kim, Jee-Hyong Lee     논문지: KSII Transactions on Internet and Information Systems     Vol.: 13     No.: 8     pp.: 4285-4299     게재일: 2019-08-01    

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