Publication Type:

Conference Paper

Source:

RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, p.283-286 (2008)

ISBN:

9781605580937

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-63449094204&partnerID=40&md5=ada03531ee33f797723bfad29e900396

Keywords:

Browsing, Explicit feedback, Information Retrieval, Information retrieval systems, Recommender systems, Search engines, Social access patterns, Social search, World Wide Web

Abstract:

Search engines are among the most-used resources on the internet. However, even today's most successful search engines struggle to provide high quality search results. According to recent studies as many as 50 percent of web search sessions fail to find any relevant results for the searcher. Researchers have proposed social search techniques, in which early searchers provide feedback that is used to improve relevance for later searchers. In this paper we investigate foundational questions of social search. In particular, we directly assess the degree of agreement among users about the relevance ranking of search results. We developed a simulated search engine interface that systematically randomizes Google's normal relevance ordering of the items presented to users. Our results show that (a) people are biased toward items in the top of the search lists, even if the list is randomized; (b) people explicit feedback is not biased and (c) people's shared preferences do not always agree with Google's result order. These results suggest that social search techniques might improve the effectiveness of web search engines. © 2008 ACM.

Notes:

cited By (since 1996)7; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@7be61497 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@3e52050d Through org.apache.xalan.xsltc.dom.DOMAdapter@7ac9b871; Conference Code:75732

Cite this Research Publication

A. Ka Agrahri, Anand, T. MbDivya, and Riedl, Ja, “Can people collaborate to improve the relevance of search results?”, in RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, 2008, pp. 283-286.