Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery 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
Quantifying Asymmetric Semantic Relations from Query Logs by Resource Allocation
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Query recommendation and its usefulness evaluation on mobile search engine
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Degree distributions of evolving alphabetic bipartite networks and their projections
Theoretical Computer Science
Concept based query recommendation
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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This paper presents a new query recommendation method that generates recommended query list by mining large-scale user logs. Starting from the user logs of click-through data, we construct a bipartite network where the nodes on one side correspond to unique queries, on the other side to unique URLs. Inspired by the bipartite network based resource allocation method, we try to extract the hidden information from the Query-URL bipartite network. The recommended queries generated by the method are asymmetrical which means two related queries may have different strength to recommend each other. To evaluate the method, we use one week user logs from Chinese search engine Sogou. The method is not only `content ignorant', but also can be easily implemented in a paralleled manner, which is feasible for commercial search engines to handle large scale user logs.