Randomized algorithms
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 11th international conference on World Wide Web
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Recommending better queries from click-through data
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
Mining query subtopics from search log data
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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We use clickthrough data of a Japanese commercial search engine to evaluate the similarity between a query and a facet category from the patterns of clicks on URLs. Using a small number of seed queries, we extract a set of topical words forming search queries together with the same facet directive words, e.g., 'recipe' in 'curry recipe' or 'apple pie recipe'. We used a PageRank-like random walk approach on query-URL bipartite graphs called "Biased ClickRank" to propagate facet attributes through click bipartite graphs. We noticed that queries to URL links are too sparse to capture query variations whereas queries to domain links are too coarse to discriminate among the different usages of broadly related queries. We introduced edges and vertices corresponding to the decomposed URL paths into the click graph to capture the click pattern differences at an appropriate granularity level. Our expanded graph model improved recalls as well as average precision against baseline graph models.