Analysis of a very large web search engine query log
ACM SIGIR Forum
Clustering user queries of a search engine
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SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Concept-based interactive query expansion
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Extracting semantic relations from query logs
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Proceedings of the 2009 workshop on Web Search Click Data
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mining Query Logs: Turning Search Usage Data into Knowledge
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A comparative analysis of query similarity metrics for community-based web search
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Generating suggestions for queries in the long tail with an inverted index
Information Processing and Management: an International Journal
Efficient query recommendations in the long tail via center-piece subgraphs
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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One important challenge of current search engines is to satisfy the users' needs when they provide a poorly formulated query. When the pages matching the user's original keywords are judged to be unsatisfactory, query recommendation techniques are used to propose alternative queries and alter the result set. These techniques search for queries that are semantically similar to the user's original query, often searching for keywords that are similar to the keywords given by the user. However, when the original query is sufficiently ill-posed, the user's informational need is best met using entirely different keywords, and a substantially different query may be necessary. We propose a novel approach that is not based on the keywords of the original query. We intentionally seek out orthogonal queries, which are related queries that have (almost) no common terms with the user's query. This allows an orthogonal query to satisfy the user's informational need when small perturbations of the original keyword set are insufficient. By using this technique to generate query recommendations, we outperform several known approaches, being the best for long tail queries.