Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Web-page summarization using clickthrough data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Characterizing typical and atypical user sessions in clickstreams
Proceedings of the 17th international conference on World Wide Web
Inferring semantic query relations from collective user behavior
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Proceedings of the 18th international conference on World wide web
Anatomy of the long tail: ordinary people with extraordinary tastes
Proceedings of the third ACM international conference on Web search and data mining
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Large-scale bot detection for search engines
Proceedings of the 19th international conference on World wide web
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
User behavior in zero-recall ecommerce queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Marco Polo: a system for brand-based shopping and exploration
Proceedings of the 20th ACM international conference on Information and knowledge management
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
Interactive pattern mining on hidden data: a sampling-based solution
Proceedings of the 21st ACM international conference on Information and knowledge management
Metaphor: a system for related search recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
On segmentation of eCommerce queries
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.