Abstract
Tag recommendation is a fundamental service in today’s social annotation systems, assisting users as they collect and annotate resources. Our previous work has demonstrated the strengths of a linear weighted hybrid, which weights and combines the results of simple components into a final recommendation. However, these previous efforts treated each user the same. In this work, we extend our approach by automatically discovering partitions of users. The user partitioning hybrid learns a different set of weights for these user partitions. Our rigorous experimental results show a marked improvement. Moreover, analysis of the partitions within a dataset offers interesting insights into how users interact with social annotations systems.
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Gemmell, J., Mobasher, B., Burke, R. (2014). User Partitioning Hybrid for Tag Recommendation. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_7
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DOI: https://doi.org/10.1007/978-3-319-08786-3_7
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