User Preference Translation Model for Next Top-k Items Recommendation with Social Relations

Hao Shang Ma, Jen Wei Huang

研究成果: Conference contribution

摘要

Recommendation systems are used to predict the interests of users through the analysis of historical preferences. Collaborative filtering-based approaches usually ignore the sequential information and sequential recommendation usually focus on the next item prediction. In this work, we would like to determine the next top-k recommendation problem. We propose User Preference Translation Model (UPTM) with item influence embedding and social relations between users. In addition, we will also solve the cold start problem in UPTM.

原文English
主出版物標題Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
編輯Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, Chih-Ya Shen
發行者Springer Science and Business Media Deutschland GmbH
頁面652-655
頁數4
ISBN(列印)9783030731991
DOIs
出版狀態Published - 2021
事件26th International Conference on Database Systems for Advanced Applications, DASFAA 2021 - Taipei, Taiwan
持續時間: 2021 四月 112021 四月 14

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12683 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
國家/地區Taiwan
城市Taipei
期間21-04-1121-04-14

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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