TideFC: Learning Temporal Interaction for Dynamic Embedding via Feature Crossing

Chang Ming Tsai, Cheng Te Li

研究成果: Conference contribution

摘要

Recommendation system is becoming more and more important. Specially in social network, such as Twitter, Facebook, and YouTube. In the beginning, a number of studies learn the relationship of users and items from a bipartite graph. They embed each user and item in an embedding space. However, they ignore temporal properties. They regard users and items as static embeddings. As time passed, user preferences and item concepts can change, and node embeddings should be dynamically adjusted in the embedding space. Nevertheless, most of existing models update embeddings only when user takes an interaction with an item. In this paper, we propose TideFC, a novel model based on the state-of-the-art dynamic embedding model JODIE. TideFC can predict the future trajectories of users' and items' embeddings. Besides, we take advantage of t-batch that creates time-consistent batches to make the training stage more efficient. More importantly, we incorporate feature crossing to generate high-order feature interactions in our TideFC. Experiments conducted on multiple real datasets demonstrate the promising performance of TideFC, compared with the state-of-the-art JODIE.

原文English
主出版物標題Proceedings - 2020 International Computer Symposium, ICS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面565-569
頁數5
ISBN(電子)9781728192550
DOIs
出版狀態Published - 2020 12月
事件2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
持續時間: 2020 12月 172020 12月 19

出版系列

名字Proceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
國家/地區Taiwan
城市Tainan
期間20-12-1720-12-19

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 電腦科學應用
  • 資訊系統
  • 資訊系統與管理
  • 計算數學

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