C4.5: programs for machine learning
C4.5: programs for machine learning
On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Patterns of search: analyzing and modeling Web query refinement
UM '99 Proceedings of the seventh international conference on User modeling
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Analysis of a very large web search engine query log
ACM SIGIR Forum
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 6th international conference on Intelligent user interfaces
Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Combining evidence for automatic web session identification
Information Processing and Management: an International Journal - Issues of context in information retrieval
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining related queries from search engine query logs
Proceedings of the 15th international conference on World Wide Web
s-grams: Defining generalized n-grams for information retrieval
Information Processing and Management: an International Journal
Defining a session on Web search engines: Research Articles
Journal of the American Society for Information Science and Technology
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
Design trade-offs for search engine caching
ACM Transactions on the Web (TWEB)
Learning about the world through long-term query logs
ACM Transactions on the Web (TWEB)
Personalized Concept-Based Clustering of Search Engine Queries
IEEE Transactions on Knowledge and Data Engineering
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
(Query) History Teaches Everything, Including the Future
LA-WEB '08 Proceedings of the 2008 Latin American Web Conference
A survey on session detection methods in query logs and a proposal for future evaluation
Information Sciences: an International Journal
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An analysis framework for search sequences
Proceedings of the 18th ACM conference on Information and knowledge management
Application of automatic topic identification on Excite Web search engine data logs
Information Processing and Management: an International Journal
How are we searching the World Wide Web? A comparison of nine search engine transaction logs
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Multitasking during Web search sessions
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Do you want to take notes?: identifying research missions in Yahoo! search pad
Proceedings of the 19th international conference on World wide web
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends 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
Proceedings of the 20th ACM international conference on Information and knowledge management
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Although Web search engines still answer user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g., finding a recipe, booking a flight, reading online news, etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a user search session), which refer to the same need. Second, we discover collective tasks by aggregating similar user tasks, possibly performed by distinct users. To discover user tasks, we propose query similarity functions based on unsupervised and supervised learning approaches. We present a set of query clustering methods that exploit these functions in order to detect user tasks. All the proposed solutions were evaluated on a manually-built ground truth, and two of them performed better than state-of-the-art approaches. To detect collective tasks, we propose four methods that cluster previously discovered user tasks, which in turn are represented by the bag-of-words extracted from their composing queries. These solutions were also evaluated on another manually-built ground truth.