Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generating unambiguous URL clusters from web search
Proceedings of the 2009 workshop on Web Search Click Data
Are Clickthroughs Useful for Image Labelling?
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Classifying Images with Image and Text Search Clickthrough Data
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Using clicks as implicit judgments: expectations versus observations
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Implicit association via crowd-sourced coselection
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
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Semantic associations take many forms, sometimes being explicit as in visible links and at other times being implicit, not visible but nevertheless clear to the human reader. Some implicit semantic associations might be calculable as the result of a computation but in some cases it is difficult for a computation to capture the purpose of a semantic association, for example, the semantic similarities embodied by synonyms and similar word/phrase likenesses are not easily specified in a general rule. It is possible however to capture semantic associations made by human searchers. Searchers interact with search results by clicking on one or more resources in a set of results, and this interaction takes two forms: the first being an implicit indication of the relevance of the search term to the chosen resource, and the second being an implicit indication of the mutual relevance of any two or more resources selected from the same search. Both have been proposed as a similarity measure for clustering of resources. In this paper we implement, evaluate and compare three methods for semantic association discovery, mined from Web search logs. The first method is based purely on query analysis, the second is single click-based, and the third is coselection-based. The methods are compared for their effectiveness at detecting semantic similarities.