Abstract
Hashtags, starting with a symbol “#” ahead of terms, are widely used and inserted anywhere within posts as they present rich sentiment information on topics that people are really interested in. In this paper, we focus on the problem of hashtag recommendation considering its personalized and evolutionary aspects. We introduce three features to model personal user interest and its evolution, including (1) hashtag popularity; (2) hashtag textual information; and (3) hashtag time factor. We construct a hybrid model combining these features to learn user preference and recommend personalized hashtags consequently.
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Cui, A., Zhang, M., Liu, Y., et al.: Discover breaking events with popular hashtags in twitter. In: CIKM 2012 Conference Proceedings, pp. 1794–1798 (2012)
Godin, F., Slavkovikj, V., Neve, W.D.: Using topic models for twitter hashtag recommendation. In: WWW 2013 Conference Proceedings, pp. 593–596 (2013)
Kywe, S.M., Hoang, T.-A., Lim, E.-P., et al.: On recommending hashtags in twitter networks. In: SocInfo 2012 Conference Proceedings, pp. 337–350 (2012)
Lehmann, J., Goncalves, B., Ramasco, J.J., et al.: Dynamical classes of collective attention in twitter. In: WWW 2012 Conference Proceedings, pp. 251–260 (2012)
Yang, L., Sun, T., Zhang, M., et al.: We know what you #tag: does the dual role affect hashtag adoption? In: WWW 2012 Conference Proceedings, pp. 261–270 (2012)
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© 2014 Springer International Publishing Switzerland
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Yu, J., Shen, Y. (2014). Evolutionary Personalized Hashtag Recommendation. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_5
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DOI: https://doi.org/10.1007/978-3-319-08010-9_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
eBook Packages: Computer ScienceComputer Science (R0)