Abstract
In this paper, we present GeniUS, a generic topic and user modeling library for the Social Semantic Web that enriches the semantics of social data and status messages particularly. Given a stream of messages, it allows for generating topic and user profiles that summarize the stream according to domain- and application-specific needs which can be specified by the requesting party. Therefore, GeniUS can be applied in various application settings. In this paper, we analyze and evaluate GeniUS in six different application domains. Given users’ status messages from Twitter, we investigate the quality of profiles that are generated by different GeniUS user modeling strategies for supporting various recommendation tasks ranging from product recommendations to more specific recommendations as required in book or software product stores. Our evaluation shows that GeniUS succeeds in inferring the semantic meaning of Twitter status messages. We prove that it can successfully adapt to a given domain and application context allowing for tremendous improvements of the recommendation quality when domain-specific semantic filtering is applied to remove noise from the profiles.
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Gao, Q., Abel, F., Houben, GJ. (2012). GeniUS: Generic User Modeling Library for the Social Semantic Web. In: Pan, J.Z., et al. The Semantic Web. JIST 2011. Lecture Notes in Computer Science, vol 7185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29923-0_11
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DOI: https://doi.org/10.1007/978-3-642-29923-0_11
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