Abstract
Microblog attracts a tremendous large number of users, and consequently affects their daily life deeply. Detecting user preference for profile construction on microblog is significant and imperative, since it facilitates not only the enhancement of users’ utilities but also the promotion of business values (e.g., online advertising, commercial recommendation). Users might be instinctively reluctant to exposure their preferences in their own published messages for the privacy protection issues. However, their preferences can never be concealed in those information they read (or subscribed), since users do need to get something useful in their readings, especially in the microblog application. Based on this observation, in this work, we successfully detect user preference, by proposing to filter out followees’ noisy postings under a dedicated commercial taxonomy, followed by clustering associated topics among followees, and finally by selecting appropriate topics as their preferences. Our extensive empirical evaluation confirms the effectiveness of our proposed method.
This work is partially supported by National Science Foundation of China under grant numbers 61003069 and 61232002, and National High-tech R&D Program (863 Program) under grant number 2012AA011003.
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References
Liu, Y., et al.: Finding the right consumer: optimizing for conversion in display advertising campaigns. In: WSDM, pp. 473–482 (2012)
Sugiyama, K., et al.: Adaptive web search based on user profile constructed without any effort from users. In: WWW, pp. 675–684 (2004)
Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)
Kwak, H., et al.: What is twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)
Weng, J., et al.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM, pp. 261–270 (2010)
Liu, K., et al.: Large-scale behavioral targeting with a social twist. In: CIKM, pp. 1815–1824 (2011)
Hannon, J., et al.: Recommending twitter users to follow using content and collaborative filtering approaches. In: RecSys, pp. 199–206 (2010)
Agarwal, D., et al.: Targeting converters for new campaigns through factor models. In: WWW, pp. 101–110 (2012)
Guy, I., et al.: Social media recommendation based on people and tags. In: SIGIR, pp. 194–201 (2010)
Banerjee, N., et al.: User interests in social media sites: an exploration with micro-blogs. In: CIKM, pp. 1823–1826 (2009)
Yang, S.H., et al.: Like like alike: joint friendship and interest propagation in social networks. In: WWW, pp. 537–546 (2011)
Adomavicius, G., et al.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
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Xu, C., Zhou, M., Chen, F., Zhou, A. (2013). Detecting User Preference on Microblog. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_16
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DOI: https://doi.org/10.1007/978-3-642-37450-0_16
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