Learning latent perception graphs for personalized unknowns recommendation

Lo Pang Yun Ting, Shan Yun Teng, Suhang Wang, Kun Ta Chuang, Huan Liu

研究成果: Conference contribution

摘要

The fast-growing online-learning platforms, which are very convenient and contain rich course resources, have attracted many users to explore new knowledge online. However, the learning quality of online-learning is generally not as effective as offline classes. In offline studies in classrooms, teachers can interact with students and teach students in accordance with personal aptitude from students' feedback in classes. Without such real-time interaction, it is difficult for users to be aware of personal unknowns. In this paper, we consider an important issue to discover 'user unknowns' from the question-giving process in online-learning platforms. A novel personalized learning framework, called PagBay, is devised to recommend user unknowns in the iterative round-by-round strategy, which contributes to applications such as a conversational bot. The flow enables users to progressively discover their weakness and to help them progress. However, discovering personal unknowns is quite challenging in online-learning platforms. Even though solving the problem with previous recommender algorithms provides solutions, they often lead to suboptimal results for unknowns recommendation as they simply rely on the user ratings and contextual features of questions. Generally, questions are associated with perceptions, and mining the relationships among users, questions, and perceptions potentially provide the clue to the better unknowns recommendation. Therefore, in this paper, we develop a novel recommender framework by borrowing strengths from perception-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in online learning services.

原文English
主出版物標題Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面32-41
頁數10
ISBN(電子)9781728141442
DOIs
出版狀態Published - 2020 十月
事件2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020 - Virtual, Atlanta, United States
持續時間: 2020 十二月 12020 十二月 3

出版系列

名字Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020

Conference

Conference2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
國家United States
城市Virtual, Atlanta
期間20-12-0120-12-03

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Cognitive Neuroscience

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