Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
Towards an adaptive and task-specific ranking mechanism in Web searching (poster session)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 6th international conference on Intelligent user interfaces
ACM Transactions on Internet Technology (TOIT)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Search and Ranking Algorithms for Locating Resources on the World Wide Web
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The Use of Implicit Evidence for Relevance Feedback in Web Retrieval
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An analysis of web searching by European AlltheWeb.com users
Information Processing and Management: an International Journal
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
DS'05 Proceedings of the 8th international conference on Discovery Science
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A number of explicit and implicit feedback mechanisms have been proposed to improve the quality of the search engine results. The current approaches to information retrieval depends heavily on the web linkage structure which is a form of relevance judgment by the page authors. However, to overcome spamming attempts and the huge volumes of data, it is important to also incorporate the user feedback on the page relevance of a document. Since users hardly give explicit/direct feedback on search quality, it becomes necessary to consider implicit feedback that can be collected from search engine logs. In this article we evaluate two implicit feedback measures, namely click sequence and time spent in reading a document. We develop a mathematical programming model to collate the feedback collected from different sessions into a partial rank ordering of documents. The two implicit feedback measures, namely the click sequence and time spent in reading a document are compared for their feedback information content using Kendall's t measure. Experimental results based on actual log data from AlltheWeb.com demonstrate that these two relevance judgment measures are not in perfect aggrement and hence incremental information can be derived from them.