Recommending topics in dialogue

Yi Chung Chen, Ming Yeh Tsai, Chiang Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1165-1185
Number of pages21
JournalWorld Wide Web
Volume21
Issue number5
DOIs
Publication statusPublished - 2018 Sep 1

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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