Paper Info
Title
Social Search With Missing Data: Which Ranking Algorithm?
Abstract
Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user- profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods.
Year
Venue
Keywords
2007
JDIM
relation discovery,ranking algorithms,social software,instant messaging.,rank correlation,statistical significance,missing data,web pages,mutual information
Field
DocType
Volume
Ontology,Data mining,Social network,Web page,Computer science,Artificial intelligence,Missing data,Markup language,Ranking,Information retrieval,Social search,Algorithm,Mutual information,Machine learning
Journal
5
Issue
Citations 
PageRank 
5
0
0.34
References 
Authors
15
6
Name
Order
Citations
PageRank
Jianhan Zhu147428.87
Marc Eisenstadt234271.18
Alexandre L. Gonçalves3176.22
Chris Denham440.84
Victoria Uren5118478.67
Dawei Song647245.59