Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Web Structure, Dynamics and Page Quality
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Information search and re-access strategies of experienced web users
WWW '05 Proceedings of the 14th international conference on World Wide Web
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
VLDB '05 Proceedings of the 31st international conference on Very large data bases
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Are people biased in their use of search engines?
Communications of the ACM - Alternate reality gaming
SearchBar: a search-centric web history for task resumption and information re-finding
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CoSearch: a system for co-located collaborative web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Ranking Tagged Resources Using Social Semantic Relevance
International Journal of Information Retrieval Research
Focused crawling of tagged web resources using ontology
Computers and Electrical Engineering
Hi-index | 0.00 |
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.