Search

This paper provides empirical support for some of the key assumptions guiding the design of data fusion methods. It computes and analyzes the overlap structures between the search results of retrieval systems that participated in the short, long, and manual tracks in TREC 3, 6, 7, and 8 to examine what can be learned to infer a document's probability of being relevant. This paper shows that the potential relevance of a document increases exponentially as the number of systems retrieving it increases—called the Authority Effect. It also shows that documents higher up in ranked lists and found by more systems are more likely to be relevant—called the Ranking Effect. A contribution of this paper is that it shows that the Authority and Ranking Effects can be observed regardless of whether a query is generated manually or automatically and short or long queries are used. Further, it is illustrated that the Authority and Ranking Effects can be observed if the result sets of random groupings of five retrieval systems are compared and only the top 50 results are used in the overlap computation. Also discussed is how the Authority and Ranking Effects can help explain why major data fusion methods perform well. [ABSTRACT FROM AUTHOR]

This paper analyzes which pages and topics are the most popular on Wikipedia and why. For the period of September 2006 to January 2007, the 100 most visited Wikipedia pages in a month are identified and categorized in terms of the major topics of interest. The observed topics are compared with search behavior on the Web. Search queries, which are identical to the titles of the most popular Wikipedia pages, are submitted to major search engines and the positions of popular Wikipedia pages in the top 10 search results are determined. The presented data helps to explain how search engines, and Google in particular, fuel the growth and shape what is popular on Wikipedia. [ABSTRACT FROM AUTHOR]