Ranking in folksonomy systems: can context help?

ABSTRACT

Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.

REFERENCES

• 1: F. Abel, M. Frank, N. Henze, D. Krause, D. Plappert, and P. Siehndel. GroupMe! - Where Semantic Web meets Web 2.0. In Proc. of ISWC '07), November 2007.
• 2: F. Abel, N. Henze, and D. Krause. Analyzing Ranking Algorithms in Folksonomy Systems. Technical report, L3S Research Center, 2008.
• 3: Shenghua Bao , Guirong Xue , Xiaoyuan Wu , Yong Yu , Ben Fei , Zhong Su, Optimizing web search using social annotations, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242640]
• 4: A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. FolkRank: A Ranking Algorithm for Folksonomies. In Proc. of FGIR '06, Germany, 2006.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.m MISCELLANEOUS

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
folksonomies, groupme!, ranking, recommender systems, search, social media