TY - GEN
T1 - Inter-Profile Similarity (IPS)
T2 - 1st International Conference on Complex Sciences: Theory and Applications, Complex 2009
AU - Spear, Matt
AU - Lu, Xiaoming
AU - Matloff, Norman S.
AU - Wu, S. Felix
PY - 2009
Y1 - 2009
N2 - Online Social Networks (OSN) are experiencing an explosive growth rate and are becoming an increasingly important part of people's lives. There is an increasing desire to aid online users in identifying potential friends, interesting groups, and compelling products to users. These networks have offered researchers almost total access to large corpora of data. An interesting goal in utilizing this data is to analyze user profiles and identify how similar subsets of users are. The current techniques for comparing users are limited as they require common terms to be shared by users. We present a simple and novel extension to a word-comparison algorithm [6], entitled Inter-Profile Similarity (IPS), which allows comparison of short text phrases even if they share no common terms. The output of IPS is simply a scalar value in [0, 1], with 1 denoting complete similarity and 0 the opposite. Therefore it is easy to understand and can provide a total ordering of users. We, first, evaluated the effectiveness of IPS with a user-study, and then applied it to datasets from Facebook and Orkut verifying and extending earlier results. We show that IPS yields both a larger range for the similarity value and obtains a higher value than intersection-based mechanisms. Both IPS and the output from the analysis of the two OSN should help to predict and classify social links, make recommendations, and annotate friends relations for social network analysis.
AB - Online Social Networks (OSN) are experiencing an explosive growth rate and are becoming an increasingly important part of people's lives. There is an increasing desire to aid online users in identifying potential friends, interesting groups, and compelling products to users. These networks have offered researchers almost total access to large corpora of data. An interesting goal in utilizing this data is to analyze user profiles and identify how similar subsets of users are. The current techniques for comparing users are limited as they require common terms to be shared by users. We present a simple and novel extension to a word-comparison algorithm [6], entitled Inter-Profile Similarity (IPS), which allows comparison of short text phrases even if they share no common terms. The output of IPS is simply a scalar value in [0, 1], with 1 denoting complete similarity and 0 the opposite. Therefore it is easy to understand and can provide a total ordering of users. We, first, evaluated the effectiveness of IPS with a user-study, and then applied it to datasets from Facebook and Orkut verifying and extending earlier results. We show that IPS yields both a larger range for the similarity value and obtains a higher value than intersection-based mechanisms. Both IPS and the output from the analysis of the two OSN should help to predict and classify social links, make recommendations, and annotate friends relations for social network analysis.
UR - https://www.scopus.com/pages/publications/84885891946
UR - https://www.scopus.com/pages/publications/84885891946#tab=citedBy
U2 - 10.1007/978-3-642-02466-5_31
DO - 10.1007/978-3-642-02466-5_31
M3 - Conference contribution
AN - SCOPUS:84885891946
SN - 3642024653
SN - 9783642024658
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
SP - 320
EP - 333
BT - Complex Sciences - First International Conference, Complex 2009, Revised Papers
Y2 - 23 February 2009 through 25 February 2009
ER -