{"type":"ScholarlyArticle","title":"Learning User Clicks in Web Search","authors":[{"name":"Ding Zhou","slug":"ding-zhou","given":"Ding","family":"Zhou","affiliation":"Department of Computer Science and Engineering; Department of Statistics College of Information Sciences and Technology The Pennsylvania State University , University Park , PA 16802"},{"name":"Levent Bolelli","slug":"levent-bolelli","given":"Levent","family":"Bolelli","affiliation":"Department of Computer Science and Engineering; Department of Statistics College of Information Sciences and Technology The Pennsylvania State University , University Park , PA 16802"},{"name":"Jia Li","slug":"jia-li-2","given":"Jia","family":"Li","affiliation":"Department of Computer Science and Engineering; Department of Statistics College of Information Sciences and Technology The Pennsylvania State University , University Park , PA 16802"},{"name":"C. Lee Giles","slug":"c-lee-giles","given":"C. Lee","family":"Giles","affiliation":"Department of Computer Science and Engineering; Department of Statistics College of Information Sciences and Technology The Pennsylvania State University , University Park , PA 16802"},{"name":"Hongyuan Zha","slug":"hongyuan-zha","given":"Hongyuan","family":"Zha","affiliation":"Department of Computer Science and Engineering; Department of Statistics College of Information Sciences and Technology The Pennsylvania State University , University Park , PA 16802"}],"abstract":"Machine learning for predicting user clicks in Web-based search offers automated explanation of user activity. We address click prediction in the Web search scenario by introducing a method for click prediction based on observations of past queries and the clicked documents. Due to the sparsity of the problem space, commonly encountered when learning for Web search, new approaches to learn the probabilistic relationship between documents and queries are proposed. Two probabilistic models are developed, which differ in the interpretation of the query-document co-occurrences. A novel technique, namely, conditional probability hierarchy, flexibly adjusts the level of granularity in parsing queries, and, as a result, leverages the advantages of both models.","doi":null,"url":"https://ijcai.org/proceedings/2007/188","pdf":"https://ijcai.org/proceedings/2007/0188.pdf","markdown":"https://ijcai.org/proceedings/2007/188.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/188","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":188,"pages":{"first":1162,"last":1166},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Banerjee and Ghosh, 2001] A. Banerjee and J. Ghosh. Clickstream clustering using weighted longest common subsequences. In In Proceedings of the Web Mining Work- shop at the 1 st SIAM Conference on Data Mining, 2001.","title":"Clickstream clustering using weighted longest common subsequences","authors":"Ghosh Banerjee, A Banerjee, J Ghosh","year":"2001","venue":"Proceedings of the Web Mining Workshop at the 1 st SIAM Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Beeferman and Berger, 2000] Doug Beeferman and Adam Berger. Agglomerative clustering of a search engine query log. In KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discov- ery and data mining, 2000.","title":"Agglomerative clustering of a search engine query log","authors":"Berger Beeferman, Doug Beeferman, Adam Berger","year":"2000","venue":"KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Deshpande and Karypis, 2004] Mukund Deshpande and George Karypis. Selective markov models for pre- dicting web page accesses. ACM Trans. Inter. Tech., 4(2):163-184, 2004.","title":"Selective markov models for predicting web page accesses","authors":"Mukund Deshpande, George Karypis","year":"2004","venue":"ACM Trans. Inter. Tech","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gunduz and Ozsu, 2003] Sule Gunduz and M. Tamer Ozsu. A web page prediction model based on click-stream tree representation of user behavior. In KDD '03: Proceed- ings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003.","title":"A web page prediction model based on click-stream tree representation of user behavior","authors":"Sule Gunduz, M Tamer Ozsu","year":"2003","venue":"KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Halvey et al., 2005] Martin Halvey, Mark T. Keane, and Barry Smyth. Predicting navigation patterns on the mobile-internet using time of the week. In WWW '05: Spe- cial interest tracks and posters of the 14th international conference on World Wide Web, pages 958-959, 2005.","title":"Predicting navigation patterns on the mobile-internet using time of the week","authors":"Martin Halvey, Mark T Keane, Barry Smyth","year":"2005","venue":"WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Joachims, 2002] Thorsten Joachims. Optimizing search en- gines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002.","title":"Optimizing search engines using clickthrough data","authors":"Thorsten Joachims","year":"2002","venue":"KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Shen et al., 2005] Xuehua Shen, Susan Dumais, and Eric Horvitz. Analysis of topic dynamics in web search. In WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web, pages 1102- 1103, 2005.","title":"Analysis of topic dynamics in web search","authors":"Shen","year":"2005","venue":"WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Srivastava et al., 2000] Jaideep Srivastava, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. Web usage min- ing: discovery and applications of usage patterns from web data. SIGKDD Explor. Newsl., 1(2):12-23, 2000.","title":"Web usage mining: discovery and applications of usage patterns from web data","authors":"Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan","year":"2000","venue":"SIGKDD Explor. Newsl","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}