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Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007
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Social Resource Sharing The del.icio.us paradigm. Users store links to web pages of interest along with arbitrary, user-specified tags in a server. The model is independent of the resource being shared. Music (Last.fm) Photos (Flickr) Publications (CiteULike) …
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Folksonomies Folk+taxonomy. Taxonomies are rigid, carefully engineered structures. Folksonomies are flexible, time-variant structures that result from the converging use of the same vocabulary.
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Interesting Problems A wealth of interest problems in this setting: Search result ranking Personalization Recommendation Trend detection Community extraction …
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Keyword Search Result ranking is currently naïve. Resources associated with tags matching the keywords are returned in reverse chronological order. TF/IDF not useful in this context. What about PageRank™?
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PageRank Algorithm Let be a collection of web pages. Then Many alternatives in interpreting the PageRank of a web page. Iterative computation
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Formalism Entities of a Folksonomy Users U Tags T Resources R Assignments Y Representation Tripartite undirected hypergraph G=(V,E), V=UUTUR, E={ (u,t,r) | (u,t,r) in Y }
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Adapted PageRank Flatten the Folksonomy graph. Apply PageRank. A resource tagged with important tags by important users becomes important. Symmetrically for tags and users.
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Adapted PageRank Important! The flat Folksonomy graph is undirected. Part of the weight that goes through an edge at time t, will flow back at time t+1. Results are similar to an edge degree ranking. They are identical for d=1.
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FolkRank Topic specific ranking in Folksonomies. A topic is defined through preference vector A topic can be defined through tags, resources or users. Let be the Adapted PageRank vector for d=1. Let be the Adapted PageRank vector for d<1 and a specified preference vector. The FolkRank vector is.
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Results Adapted PageRank, d=1
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Results Adapted PageRank vs FolkRank
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Extensions Resource recommendation. Similar tag suggestion. User introduction. Trend detection.
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