TY - JOUR
T1 - Recommending topics in dialogue
AU - Chen, Yi Chung
AU - Tsai, Ming Yeh
AU - Lee, Chiang
N1 - Funding Information:
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.
Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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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U2 - 10.1007/s11280-017-0499-0
DO - 10.1007/s11280-017-0499-0
M3 - Article
AN - SCOPUS:85030321496
SN - 1386-145X
VL - 21
SP - 1165
EP - 1185
JO - World Wide Web
JF - World Wide Web
IS - 5
ER -