Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS,...
-
Upload
junior-taylor -
Category
Documents
-
view
214 -
download
0
Transcript of Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS,...
![Page 1: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/1.jpg)
Information Retrieval in Folksonomies
Nikos Sarkas
Social Information Systems Seminar
DCS, University of Toronto, Winter 2007
![Page 2: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/2.jpg)
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) …
![Page 3: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/3.jpg)
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.
![Page 4: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/4.jpg)
Interesting Problems
A wealth of interest problems in this setting: Search result ranking Personalization Recommendation Trend detection Community extraction …
![Page 5: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/5.jpg)
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™?
![Page 6: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/6.jpg)
PageRank Algorithm
Let be a collection of web pages. Then
Many alternatives in interpreting the
PageRank of a web page. Iterative computation
1,..., nP P
( )
( )( )
( )j i
ji
P M P j
PR PPR P
L P
1 (1 )t tw dAw d p
![Page 7: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/7.jpg)
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 }
![Page 8: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/8.jpg)
Adapted PageRank
Flatten the Folksonomy graph.
Apply PageRank. A resource tagged with
important tags by important users becomes important. Symmetrically for tags and users.
2 1
1
11
U1
U2 T2
T1 R1
R2
U1
U2
T1
T2
R1
R2
12
1
1 1
![Page 9: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/9.jpg)
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.
![Page 10: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/10.jpg)
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 .
p
0w
1w
1 0:w w w
![Page 11: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/11.jpg)
Results
Adapted PageRank, d=1
![Page 12: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/12.jpg)
Results
Adapted PageRank vs FolkRank
![Page 13: Information Retrieval in Folksonomies Nikos Sarkas Social Information Systems Seminar DCS, University of Toronto, Winter 2007.](https://reader035.fdocuments.in/reader035/viewer/2022062518/56649e9f5503460f94ba0b8d/html5/thumbnails/13.jpg)
Extensions
Resource recommendation. Similar tag suggestion. User introduction. Trend detection.