Exploiting session-like behaviors in tag prediction

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.

• Publication year

  2011

• Venue

  The Web Conference

• Publication date

  2011-03-28

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/1963192.1963277
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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