메뉴 건너뛰기




Volumn , Issue , 2009, Pages 1037-1045

Mining rich session context to improve web search

Author keywords

Aggregate user behavior; Clickrank; Intentional surfer model; Learning to rank; Web search

Indexed keywords

AGGREGATE MODEL; CLICKRANK; COMPUTATIONAL COSTS; CONTEXTUAL INFORMATION; INTENTIONAL SURFER MODEL; LAY-OUT; LEARNING TO RANK; NOVEL APPLICATIONS; OTHER APPLICATIONS; RETRIEVAL PERFORMANCE; SCALABLE ALGORITHMS; THEORETICAL FOUNDATIONS; USER SESSIONS; WEB PAGE; WEB SEARCH; WEB SEARCHES; WEB USERS;

EID: 70350649010     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557131     Document Type: Conference Paper
Times cited : (13)

References (39)
  • 1
    • 35348877424 scopus 로고    scopus 로고
    • Why we search: Visualizing and predicting user behavior
    • E. Adar, D. S. Weld, B. N. Bershad, and S. S. Gribble. Why we search: visualizing and predicting user behavior. In WWW, pages 161-170, 2007.
    • (2007) , pp. 161-170
    • Adar, E.1    Weld, D.S.2    Bershad, B.N.3    Gribble, S.S.4
  • 2
    • 33750341480 scopus 로고    scopus 로고
    • Improving web search ranking by incorporating user behavior information
    • E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR, pages 19-26, 2006.
    • (2006) SIGIR , pp. 19-26
    • Agichtein, E.1    Brill, E.2    Dumais, S.3
  • 3
    • 33749571437 scopus 로고    scopus 로고
    • Identifying "best bet" web search results by mining past user behavior
    • E. Agichtein and Z. Zheng. Identifying "best bet" web search results by mining past user behavior. In KDD, pages 902-908, 2006.
    • (2006) KDD , pp. 902-908
    • Agichtein, E.1    Zheng, Z.2
  • 5
    • 57349172643 scopus 로고    scopus 로고
    • Mining the search trails of surfing crowds: Identifying relevant websites from user activity
    • M. Bilenko and R. W. White. Mining the search trails of surfing crowds: Identifying relevant websites from user activity. In WWW, pages 51-60, 2008.
    • (2008) , pp. 51-60
    • Bilenko, M.1    White, R.W.2
  • 8
    • 70350635329 scopus 로고    scopus 로고
    • Efficient PageRank approximation via graph aggregation
    • A. Z. Broder, R. Lempel, F. Maghoul, and J. Pedersen. Efficient PageRank approximation via graph aggregation. In WWW, pages 484-485, 2004.
    • (2004) , pp. 484-485
    • Broder, A.Z.1    Lempel, R.2    Maghoul, F.3    Pedersen, J.4
  • 10
    • 84885604032 scopus 로고    scopus 로고
    • Relevance weighting for query independent evidence
    • N. Craswell, S. Robertson, H. Zaragoza, and M. Taylor. Relevance weighting for query independent evidence. In SIGIR, pages 416-423, 2005.
    • (2005) SIGIR , pp. 416-423
    • Craswell, N.1    Robertson, S.2    Zaragoza, H.3    Taylor, M.4
  • 11
    • 85030321143 scopus 로고    scopus 로고
    • MapReduce: Simplified data processing on large clusters
    • J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI, pages 137-150, 2004.
    • (2004) OSDI , pp. 137-150
    • Dean, J.1    Ghemawat, S.2
  • 12
    • 67650086764 scopus 로고    scopus 로고
    • Understanding the relationship between searchers' queries and information goals
    • D. Downey, D. Liebling, and S. Dumais. Understanding the relationship between searchers' queries and information goals. In CIKM, pages 449-458, 2008.
    • (2008) CIKM , pp. 449-458
    • Downey, D.1    Liebling, D.2    Dumais, S.3
  • 14
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232, 2001.
    • (2001) The Annals of Statistics , vol.29 , Issue.5 , pp. 1189-1232
    • Friedman, J.H.1
  • 15
    • 70350654191 scopus 로고    scopus 로고
    • Online, 2008
    • Google. We know the web was big. Online, 2008. http://googleblog. blogspot.com/2008/07/we-knew-web-was-big.html.
    • Google. We know the web was big
  • 16
  • 18
    • 0033645041 scopus 로고    scopus 로고
    • IR evaluation methods for retrieving highly relevant documents
    • K. Järvelin and J. Kekäläinen. IR evaluation methods for retrieving highly relevant documents. In SIGIR, pages 41-48, 2000.
    • (2000) SIGIR , pp. 41-48
    • Järvelin, K.1    Kekäläinen, J.2
  • 19
    • 1842637192 scopus 로고    scopus 로고
    • Cumulated gain-based evaluation of IR techniques
    • K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4):422-446, 2002.
    • (2002) ACM Trans. Inf. Syst , vol.20 , Issue.4 , pp. 422-446
    • Järvelin, K.1    Kekäläinen, J.2
  • 20
    • 0242456822 scopus 로고    scopus 로고
    • Optimizing search engines using clickthrough data
    • T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133-142, 2002.
    • (2002) KDD , pp. 133-142
    • Joachims, T.1
  • 21
    • 84885665252 scopus 로고    scopus 로고
    • Accurately interpreting clickthrough data as implicit feedback
    • T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR, pages 154-161, 2005.
    • (2005) SIGIR , pp. 154-161
    • Joachims, T.1    Granka, L.2    Pan, B.3    Hembrooke, H.4    Gay, G.5
  • 22
    • 0012435995 scopus 로고    scopus 로고
    • A probabilistic model of information retrieval: Development and comparative experiments (parts 1 and 2)
    • K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: Development and comparative experiments (parts 1 and 2). Information Processing and Management, 36(6):779-840, 2000.
    • (2000) Information Processing and Management , vol.36 , Issue.6 , pp. 779-840
    • Jones, K.S.1    Walker, S.2    Robertson, S.E.3
  • 23
    • 4243148480 scopus 로고    scopus 로고
    • Authoritative sources in a hyperlinked environment
    • J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604-632, 1999.
    • (1999) Journal of the ACM , vol.46 , Issue.5 , pp. 604-632
    • Kleinberg, J.1
  • 25
    • 57549108344 scopus 로고    scopus 로고
    • Learning query intent from regularized click graphs
    • X. Li, Y.-Y. Wang, and A. Acero. Learning query intent from regularized click graphs. In SIGIR, pages 339-346, 2008.
    • (2008) SIGIR , pp. 339-346
    • Li, X.1    Wang, Y.-Y.2    Acero, A.3
  • 26
    • 57349156602 scopus 로고    scopus 로고
    • Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. BrowseRank: Letting web users vote for page importance. In SIGIR, pages 451-458, 2008.
    • Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. BrowseRank: Letting web users vote for page importance. In SIGIR, pages 451-458, 2008.
  • 27
    • 34250684611 scopus 로고    scopus 로고
    • A uniform approach to accelerated PageRank computation
    • F. McSherry. A uniform approach to accelerated PageRank computation. In WWW, pages 575-582, 2005.
    • (2005) , pp. 575-582
    • McSherry, F.1
  • 31
    • 57349141405 scopus 로고    scopus 로고
    • Recrawl scheduling based on information longevity
    • C. Olston and S. Pandey. Recrawl scheduling based on information longevity. In WWW, pages 437-446, 2008.
    • (2008) , pp. 437-446
    • Olston, C.1    Pandey, S.2
  • 32
    • 0003780986 scopus 로고    scopus 로고
    • The PageRank Citation Ranking: Bringing Order to The web
    • Technical Report, Stanford University
    • L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to The web. Technical Report, Stanford University, 1998.
    • (1998)
    • Page, L.1    Brin, S.2    Motwani, R.3    Winograd, T.4
  • 33
    • 63449107122 scopus 로고    scopus 로고
    • Predictive user click models based on click-through history
    • B. Piwowarski and H. Zaragoza. Predictive user click models based on click-through history. In CIKM, pages 175-182, 2007.
    • (2007) CIKM , pp. 175-182
    • Piwowarski, B.1    Zaragoza, H.2
  • 34
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336):846-850, 1971.
    • (1971) Journal of the American Statistical Association , vol.66 , Issue.336 , pp. 846-850
    • Rand, W.M.1
  • 35
    • 19944362539 scopus 로고    scopus 로고
    • Understanding user goals in web search
    • D. E. Rose and D. Levinson. Understanding user goals in web search. In WWW, pages 13-19, 2004.
    • (2004) , pp. 13-19
    • Rose, D.E.1    Levinson, D.2
  • 37
    • 33749567020 scopus 로고    scopus 로고
    • Mining long-term search history to improve search accuracy
    • B. Tan, X. Shen, and C. Zhai. Mining long-term search history to improve search accuracy. In KDD, pages 718-723, 2006.
    • (2006) KDD , pp. 718-723
    • Tan, B.1    Shen, X.2    Zhai, C.3
  • 38
    • 47249148884 scopus 로고    scopus 로고
    • Leveraging popular destinations to enhance web search interaction
    • R. W. White, M. Bilenko, and S. Cucerzan. Leveraging popular destinations to enhance web search interaction. ACM Trans. Web, 2(3):1-30, 2008.
    • (2008) ACM Trans. Web , vol.2 , Issue.3 , pp. 1-30
    • White, R.W.1    Bilenko, M.2    Cucerzan, S.3
  • 39
    • 35348927568 scopus 로고    scopus 로고
    • Investigating behaviorial variability in web search
    • R. W. White and S. M. Drucker. Investigating behaviorial variability in web search. In WWW, pages 21-30, 2007.
    • (2007) , pp. 21-30
    • White, R.W.1    Drucker, S.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.