Recommending topics in dialogue

Yi Chung Chen, Ming Yeh Tsai, Chiang Lee

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Several types of online chat system have been developed; however, there exist no recommendation systems for the recommendation of topics suitable for discussion with a given individual at a particular time. This paper proposes a hot-topic recommendation system based on analysis of the tweets posted by the user, his/her chat partners, and similar users of his/her chat partners, as well as hashtags trending in Twitter. In experiments, the proposed system, which is based on the well-known Latent Dirichlet Allocation (LDA) algorithm, was shown to outperform existing recommendation systems with regard to computational efficiency as well as prediction accuracy.

原文English
頁(從 - 到)1165-1185
頁數21
期刊World Wide Web
21
發行號5
DOIs
出版狀態Published - 2018 9月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 硬體和架構
  • 電腦網路與通信

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