Marek Lipczak, Börkur Sigurbjörnsson, Alejandro Jaimes.

In: Proceedings of the 23rd ACM conference on Hypertext and social media (HT’12)

Abstract

In most social networks, measuring similarity between users is crucial for providing new functionalities, understanding the dynamics of such networks, and growing them (e.g., people you may know recommendations depend on similarity, as does link prediction). In this paper, we study a large sample of Flickr user actions and compare tags across different explicit and implicit network relations. In particular, we compare tag similarities in explicit networks (based on contact, friend, and family links), and implicit networks (created by actions such as comments and selecting favorite photos). We perform an in-depth analysis of these five types of links specifically focusing on tagging, and compare different tag similarity metrics. Our motivation is that understanding the differences in such networks, as well as how different similarity metrics perform, can be useful in similarity-based recommendation applications (e.g., collaborative filtering), and in traditional social network analysis problems (e.g., link prediction). We specifically show that different types of relationships require different similarity metrics. Our findings could lead to the construction of better user models, among others.

Bibtex

@inproceedings{Lipczak:2012:ULT:2309996.2310035,
 author = {Lipczak, Marek and Sigurbjornsson, Borkur and Jaimes, Alejandro},
 title = {Understanding and leveraging tag-based relations in on-line social networks},
 booktitle = {Proceedings of the 23rd ACM conference on Hypertext and social media},
 series = {HT '12},
 year = {2012},
 isbn = {978-1-4503-1335-3},
 location = {Milwaukee, Wisconsin, USA},
 pages = {229--238},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2309996.2310035},
 doi = {10.1145/2309996.2310035},
 acmid = {2310035},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {explicit social networks, implicit social networks, social tagging, user modeling, user similarity},
}