Conference articles
Tag-based Social Interest Discovery.
In:
Proceedings of the 17th International World Wide Web Conference, pages 675-684.
ACM, 2008.
Xin Li, Lei Guo and Yihong E. Zhao.
[doi]
[abstract]
[BibTeX]
The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the diffi- culty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections. In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.
Mining Association Rules in Folksonomies.
In: V. Batagelj, H.-H. Bock, A. Ferligoj and A. Žiberna, editors,
Data Science and Classification (Proc. IFCS 2006 Conference), series Studies in Classification, Data Analysis, and Knowledge Organization, pages 261-270.
Springer, Berlin/Heidelberg, 2006.
Ljubljana
Christoph Schmitz, Andreas Hotho, Robert Jäschke and Gerd Stumme.
[doi]
[BibTeX]
Exploratory Mining and Pruning Optimizations of Constrained Association Rules..
In:
SIGMOD Conference, pages 13-24.
1998.
Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han and Alex Pang.
[doi]
[BibTeX]
Journal articles
An effective hash-based algorithm for mining association rules.
Proceedings of the 1995 ACM SIGMOD international conference on Management of data:175-186, 1995.
J.S. Park, M.S. Chen and P.S. Yu.
[BibTeX]
Conference articles
Mining Generalized Association Rules.
In:
Proceedings of the 21st International Conference on Very Large Databases, pages 407-419.
1995.
R. Srikant and R. Agrawal.
[BibTeX]
Mining association rules between sets of items in large databases.
In:
SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pages 207-216.
ACM Press, New York, NY, USA, 1993.
Rakesh Agrawal, Tomasz Imielinski and Arun Swami.
[BibTeX]