CARE: Learning convolutional attentional recurrent embedding for sequential recommendation

Yu Che Tsai, Cheng Te Li

研究成果: Conference contribution

摘要

Top-N sequential recommendation is to predict the next few items based on user's sequential interactions with past items. This paper aims at boosting the performance of top-N sequential recommendation based on a state-of-the-art model, Caser. We point out three insufficiencies of Caser - do not model variant-sized sequential patterns, treating the impact of each past time step equally, and cannot learn cumulative features. Then we propose a novel Convolutional Attentional Recurrent Embedding (CARE) learning model. Experiments conducted on a large-scale user-location check-in dataset exhibit promising performance, comparing to Caser.

原文English
主出版物標題Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
編輯Michele Coscia, Alfredo Cuzzocrea, Kai Shu
發行者Association for Computing Machinery, Inc
頁面654-660
頁數7
ISBN(電子)9781450391283
DOIs
出版狀態Published - 2021 11月 8
事件13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 - Virtual, Online, Netherlands
持續時間: 2021 11月 8 → …

出版系列

名字Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021

Conference

Conference13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
國家/地區Netherlands
城市Virtual, Online
期間21-11-08 → …

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 一般社會科學
  • 電腦科學應用
  • 軟體

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