The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Sampling search-engine results
WWW '05 Proceedings of the 14th international conference on World Wide Web
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Studying the use of popular destinations to enhance web search interaction
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
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
How people recall, recognize, and reuse search results
ACM Transactions on Information Systems (TOIS)
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Context sensitive synonym discovery for web search queries
Proceedings of the 18th ACM conference on Information and knowledge management
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Learning to suggest: a machine learning framework for ranking query suggestions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Automatic suggestion of query-rewrite rules for enterprise search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Assistance technology is undoubtedly one of the important elements in the commercial search engines, and routing the user towards the right direction throughout the search sessions is of great importance for providing a good search experience. Most search assistance methods in the literature that involve query generation, query expansion and other techniques consider each suggestion candidate individually, which implies an independence assumption. We challenge this independence assumption and give a method to maximize the utility of a given set of suggestions. For this, we will define a measure of conditional utility for query pairs using query-URL bipartite graphs based on the session logs (clicked and viewed URLs). Afterwards, we remove the redundant queries from the suggestion set using a greedy algorithm to be able to replace them with more useful ones. Both offline (based on user studies and session log analysis) and online (based on millions of user interactions) evaluations show that modeling the conditional utility and maximizing the utility of the set of queries (by eliminating redundant ones) significantly increases the effectiveness of the search assistance both for the presubmit and postsubmit modes.