|Title||Applications of Voting Theory to Information Mashups|
|Publication Type||Conference Paper|
|Year of Publication||2008|
|Authors||Christine Robson, Jan Pieper, Nachiketa Sahoo, Alfredo Alba, Meenakshi Nagarajan, Daniel Gruhl, Varun Bhagwan, Julia Grace, Kevin Haas|
|Conference Name||2nd IEEE International Conference on Semantic Computing|
|Conference Location||Santa Clara, CA, USA|
Blogs, discussion forums and social networking sites are an excellent source for people's opinions on a wide range of topics. We examine the application of voting theory to 'Information Mashups' - the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments and listening behavior from several Web communities. We consider different voting systems as algorithms to combine opinions from multiple sources and evaluate their effectiveness using social welfare functions. Different voting schemes are found to work better in some applications than others. We observe a tradeoff between broad popularity of established artists versus emerging superstars that may only be popular in one community. Overall, we find that voting theory provides a solid foundation for information mashups in this domain.
|Full Text|| |
A. Alba, V. Bhagwan, J. Grace, D. Gruhl, K. Haas, M. Nagarajan, J. Pieper, C. Robson, and N. Sahoo, 'Applications of Voting Theory to Information Mashups' in Proceedings of the 2nd IEEE International Conference on Semantic Computing, 10-17, Santa Clara, CA, August 4-7, 2008.