Journal of the American Society for Information Science and Technology
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Query suggestion based on user landing pages
SIGIR '07 Proceedings of the 30th 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
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the 18th international conference on World wide web
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
Transitive history-based query disambiguation for query reformulation
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Identifying task-based sessions in search engine query logs
Proceedings of the fourth ACM international conference on Web search and data mining
Modeling and analysis of cross-session search tasks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Query suggestions in the absence of query logs
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
Evaluating the effectiveness of search task trails
Proceedings of the 21st international conference on World Wide Web
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
New assessment criteria for query suggestion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that on-task context is helpful on average but can be easily overwhelmed when off-task queries are interleaved---a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context. We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search.