Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Users, Queries and Documents: A Unified Representation for Web Mining
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A unified representation of web logs for mining applications
Information Retrieval
Collaborative cyberporn filtering with collective intelligence
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative blacklist generation via searches-and-clicks
Proceedings of the 20th ACM international conference on Information and knowledge management
Mining search intents for collaborative cyberporn filtering
Journal of the American Society for Information Science and Technology
Objectionable content filtering by click-through data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use this behavior graph for classification. We choose a click graph sampled from two weeks of image search activity, and the task of "adult" filtering: identifying content in the graph that is inappropriate for minors. We show how to perform classification using random walks on this graph, and two methods for estimating classifier parameters.