Unraveling the Taste Fabric of Social Networks

doi:10.4018/jswis.2006010102

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Unraveling the Taste Fabric of Social Networks

Hugo Liu, Pattie Maes, Glorianna Davenport

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)2(1)

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MLA

Liu, Hugo, et al. "Unraveling the Taste Fabric of Social Networks." IJSWIS vol.2, no.1 2006: pp.42-71. http://doi.org/10.4018/jswis.2006010102

APA

Liu, H., Maes, P., & Davenport, G. (2006). Unraveling the Taste Fabric of Social Networks. International Journal on Semantic Web and Information Systems (IJSWIS), 2(1), 42-71. http://doi.org/10.4018/jswis.2006010102

Chicago

Liu, Hugo, Pattie Maes, and Glorianna Davenport. "Unraveling the Taste Fabric of Social Networks," International Journal on Semantic Web and Information Systems (IJSWIS) 2, no.1: 42-71. http://doi.org/10.4018/jswis.2006010102

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Abstract

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness — the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat — the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions — the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people — whose use cases are demonstrated within the context of three applications — the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

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Unraveling the Taste Fabric of Social Networks

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