IJSWIS2005-tastefabrics.2.doc

IJSWIS2005-tastefabrics.2.doc - Unraveling the Taste Fabric...

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Unraveling the Taste Fabric of Social Networks Hugo Liu, Pattie Maes, Glorianna Davenport The Media Laboratory, Massachusetts Institute of Technology e-mail: {hugo, pattie, gid}@media.mit.edu 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. Keywords Social Networks, Semantic Mediation, Culture and Taste, Ethotic Representation, Recommender Systems, Latent Semantics, User Modeling, Relational Mining, Computational Aesthetics, Psychographics. 1 Introduction Recently, an online social network phenomenon has swept over the Web—MySpace, Friendster, Orkut, thefacebook, LinkedIn—and the signs say that social networks are here to stay; they constitute the social Semantic Web . Few could have imagined it—tens of millions of Web users joining these social network sites, listing openly their online friends and enlisting offline ones too, and more often than not, specifying in great detail and with apparent exhibitionism tidbits about who they are, what music they listen to, what films they fancy. Erstwhile, computer scientists were struggling to extract user profiles by scraping personal homepages, but now, the extraction task is greatly simplified. Not only do self-described personal social network profiles avail greater detail about a user’s interests than a homepage, but on the three most popular sites, these interests are distributed across a greater spectrum of interests such as books, music, films, television shows, foods, sports, passions, profession, etc. Furthermore, the presentation of these user interests are greatly condensed. Whereas interests are sprinkled across hard-to-parse natural language text on personal homepages, the prevailing convention on social network profiles sees interests given as punctuation-delimited keywords and keyphrases (see examples of profiles in Figure 1), sorted by interest genres.
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