Abstract
Social annotation systems have become a staple of the World Wide Web, enabling users to organize their favorite resources with tags. Resource recommenders that exploit these tags to model users and resources have proven to be effective in assisting users navigate complex information spaces, particularly when combined with other approaches. In previous work, we demonstrated the power of a linear weighted hybrid that combines the weighted results of simple component recommenders. While this hybrid was able to learn how to weigh each of the components, it treated all users the same. In this work, we present a framework to automatically discover partitions of users and learn optimal weights for each partition. The experimental results on three real world data sets not only demonstrates an improvement in the accuracy of the algorithm, but also offers unique insights into how social annotation systems are used by various groups of users.
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Gemmell, J., Mobasher, B., Burke, R. (2014). Resource Recommendation in Social Annotation Systems Based on User Partitioning. In: Hepp, M., Hoffner, Y. (eds) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-10491-1_11
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DOI: https://doi.org/10.1007/978-3-319-10491-1_11
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