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. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Experimentation, Performance

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Dawei Yin, Liangjie Hong, Brian D. Davison
• Keywords: Internet Technology, Performance, Retrieval General Terms, Social Bookmarking Systems, WWW 2011