Poster Summary (2 pages)
Official ACM published version: http://dx.doi.org/10.1145/1963192.1963277
Author's version: PDF (53KB)
In social bookmarking systems, existing methods in tag prediction have shown that the performance of prediction can be significantly improved by modeling users' preferences. However, these preferences are usually treated as constant over time, neglecting the temporal factor within users' behaviors. In this paper, we study the problem of session-like behavior in social tagging systems and demonstrate that the predictive performance can be improved by considering sessions. Experiments, conducted on three public datasets, show that our session-based method can outperform baselines and two state-of-the-art algorithms significantly.
In Companion Proceedings of the 20th International World Wide Web Conference, pages 167-168, Hyderabad, India, March 2011.
© ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
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