Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Finding Similar Queries to Satisfy Searches Based on Query Traces
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
A formal study of information retrieval heuristics
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Asymmetrical query recommendation method based on bipartite network resource allocation
Proceedings of the 17th international conference on World Wide Web
Personalized Concept-Based Clustering of Search Engine Queries
IEEE Transactions on Knowledge and Data Engineering
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Query-URL bipartite based approach to personalized query recommendation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An optimization framework for query recommendation
Proceedings of the third ACM international conference on Web search and data mining
Journal of Information Science
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For a search engine, the challenge of finding relevant information from the web is becoming more and more difficult with rapid increase/change in content of the web. This difficulty further increases as queries submitted by users are general, imprecise, short and ambiguous. Relevance between user's information need and documents returned by search engine is largely dependent on the query given by them. In this paper, we have proposed a method to facilitate users with query recommendations which are the concepts related to their information needs. In this work, we have extracted concepts from the web snippets and we have proposed two weight functions to measure the relevance between query and concepts. Related concepts with different meaning are selected and recommended as query suggestions. To evaluate our method, we have used a Google middleware for the extraction of concepts. We have estimated the relevance between the query and concepts using the proposed weight functions and compared with the support of the concepts as well as with the TFIDF approach using the standard information-retrieval metrics of precision and Mean Average Precision(MAP). We show that our approach leads to gains in average precision than the other existing approach for different type of queries.