Abstract
Recommendation by example is common in contemporary Internet applications providing resources similar to a user-selected example. In this paper this task is considered as a function available within a social annotation system offering new ways to model both users and resources. Using three real-world datasets we motivate several conclusions. First, a personalized approach outperforms non-personalized approaches suggesting that users perceive the similarity between resources differently. Second, the manner in which users interact with social annotation systems vary producing datasets with variable characteristics and requiring different recommendation strategies to best satisfy their needs. Third, a hybrid recommender constructed from several component recommenders can produce superior results by exploiting multiple dimensions of the data. The hybrid remains powerful, flexible and extensible despite the underlying characteristics of the data.
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Gemmell, J., Schimoler, T., Mobasher, B., Burke, R. (2011). Recommendation by Example in Social Annotation Systems. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_18
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DOI: https://doi.org/10.1007/978-3-642-23014-1_18
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