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Recommending topics in dialogue

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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.

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Acknowledgements

This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 105-2119-M-035-002, MOST 106-2119-M-224-003, and MOST 105-2221-E-006-218.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan, Republic of China

    Yi-Chung Chen

  2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701, Taiwan, Republic of China

    Ming-Yeh Tsai & Chiang Lee

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  1. Yi-Chung Chen
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  2. Ming-Yeh Tsai
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  3. Chiang Lee
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Correspondence to Yi-Chung Chen.

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Chen, YC., Tsai, MY. & Lee, C. Recommending topics in dialogue. World Wide Web 21, 1165–1185 (2018). https://doi.org/10.1007/s11280-017-0499-0

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