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Inf. Syst."],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>\n            Although Web search engines still answer user queries with lists of\n            <jats:italic>ten blue links<\/jats:italic>\n            to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g.,\n            <jats:italic>finding a recipe<\/jats:italic>\n            ,\n            <jats:italic>booking a flight<\/jats:italic>\n            ,\n            <jats:italic>reading online news<\/jats:italic>\n            , etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify\n            <jats:italic>user tasks<\/jats:italic>\n            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\n            <jats:italic>collective tasks<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2493175.2493179","type":"journal-article","created":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T15:14:00Z","timestamp":1375370040000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["Discovering tasks from search engine query logs"],"prefix":"10.1145","volume":"31","author":[{"given":"Claudio","family":"Lucchese","sequence":"first","affiliation":[{"name":"ISTI-CNR, Pisa, Italy"}]},{"given":"Salvatore","family":"Orlando","sequence":"additional","affiliation":[{"name":"Universit\u00e0 Ca' Foscari Venezia, Italy"}]},{"given":"Raffaele","family":"Perego","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Pisa, Italy"}]},{"given":"Fabrizio","family":"Silvestri","sequence":"additional","affiliation":[{"name":"ISTI-CNR, Pisa, Italy"}]},{"given":"Gabriele","family":"Tolomei","sequence":"additional","affiliation":[{"name":"Universit\u00e0 Ca' Foscari Venezia, Italy"}]}],"member":"320","published-online":{"date-parts":[[2013,8,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409220.1409223"},{"key":"e_1_2_1_2_1","volume-title":"Modern Information Retrieval","author":"Baeza-Yates R.","unstructured":"Baeza-Yates , R. and Ribeiro-Neto , B. 1999. Modern Information Retrieval . Addison-Wesley Longman Publishing Co., Inc. , Boston, MA . Baeza-Yates, R. and Ribeiro-Neto, B. 1999. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347176"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458163"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/792550.792552"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401995"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772724"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'96)","author":"Ester M.","unstructured":"Ester , M. , Kriegel , H. P. , Sander , J. , and Xu , X . 1996. A density-based algorithm for discovering clusters in large spatial databases with noise . In Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'96) . ACM, New York, NY, 226--231. Ester, M., Kriegel, H. P., Sander, J., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'96). ACM, New York, NY, 226--231."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 6th International Conference on Asian Digital Libraries (ICADL'03)","volume":"2911","author":"Fu L.","unstructured":"Fu , L. , Goh , D. H.-L. , Foo , S.S.-B. , and Na , J . -C. 2003. Collaborative querying through a hybrid query clustering approach . In Proceedings of the 6th International Conference on Asian Digital Libraries (ICADL'03) . Lecture Notes in Computer Science , vol. 2911 , Springer-Verlag, Berlin Heidelberg, 111--122. Fu, L., Goh, D. H.-L., Foo, S.S.-B., and Na, J.-C. 2003. Collaborative querying through a hybrid query clustering approach. In Proceedings of the 6th International Conference on Asian Digital Libraries (ICADL'03). Lecture Notes in Computer Science, vol. 2911, Springer-Verlag, Berlin Heidelberg, 111--122."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 20th International Joint Conference on Artificial Intelligence. 6--12","author":"Gabrilovich E.","unstructured":"Gabrilovich , E. and Markovitch , S . 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis . In Proceedings of the 20th International Joint Conference on Artificial Intelligence. 6--12 . Gabrilovich, E. and Markovitch, S. 2007. Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. 6--12."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.01.026"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/359784.360293"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063619"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 22nd Annual Colloquium on Information Retrieval Research (BCS-IRSG). 57--66","author":"He D.","unstructured":"He , D. and G\u00f6ker , A . 2000. Detecting session boundaries from Web user logs . In Proceedings of the 22nd Annual Colloquium on Information Retrieval Research (BCS-IRSG). 57--66 . He, D. and G\u00f6ker, A. 2000. Detecting session boundaries from Web user logs. In Proceedings of the 22nd Annual Colloquium on Information Retrieval Research (BCS-IRSG). 57--66."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4573(01)00060-7"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/362248.362272"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2004.10.007"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/281250.281253"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/asi.v58:6"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2006.09.016"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458176"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2009922"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 7th International Conference on User Modeling. Springer-Verlag","author":"Lau T.","unstructured":"Lau , T. and Horvitz , E . 1999. Patterns of search: Analyzing and modeling Web query refinement . In Proceedings of the 7th International Conference on User Modeling. Springer-Verlag , Berlin, 119--128. Lau, T. and Horvitz, E. 1999. Patterns of search: Analyzing and modeling Web query refinement. In Proceedings of the 7th International Conference on User Modeling. Springer-Verlag, Berlin, 119--128."},{"key":"e_1_2_1_24_1","first-page":"265","article-title":"Combining Local Context and WordNet Similarity for Word Sense Identification. The MIT Press, Cambridge","volume":"11","author":"Leacock C.","year":"1998","unstructured":"Leacock , C. and Chodorow , M. 1998 . Combining Local Context and WordNet Similarity for Word Sense Identification. The MIT Press, Cambridge , MA , 11 , 265 -- 283 . Leacock, C. and Chodorow, M. 1998. Combining Local Context and WordNet Similarity for Word Sense Identification. 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Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, L. M. Le Cam and J. Neyman Eds., Vol. 1. University of California Press, Berkeley, CA, 281--297."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646284"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the 22nd Conference on Artificial Intelligence (AAAI'08)","author":"Milne D.","unstructured":"Milne , D. and Witten , I. H . 2008. An effective, low-cost measure of semantic relatedness obtained from wikipedia links . In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI'08) . AAAI Press, Menlo Park, CA, 25--30. Milne, D. and Witten, I. H. 2008. An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI'08). 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