Presentation is loading. Please wait.

Presentation is loading. Please wait.

Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.

Similar presentations


Presentation on theme: "Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007."— Presentation transcript:

1 Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007

2 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) …

3 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.

4 Interesting Problems A wealth of interest problems in this setting: Search result ranking Personalization Recommendation Trend detection Community extraction …

5 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™?

6 PageRank Algorithm Let be a collection of web pages. Then Many alternatives in interpreting the PageRank of a web page. Iterative computation

7 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 }

8 Adapted PageRank Flatten the Folksonomy graph. Apply PageRank. A resource tagged with important tags by important users becomes important. Symmetrically for tags and users.

9 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.

10 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.

11 Results Adapted PageRank, d=1

12 Results Adapted PageRank vs FolkRank

13 Extensions Resource recommendation. Similar tag suggestion. User introduction. Trend detection.


Download ppt "Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007."

Similar presentations


Ads by Google