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时间敏感查询词补全关键技术研究综述

Research Reviewof Time-Sensitive Query Auto-completion Technique
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摘要 搜索引擎的查询词补全技术给搜索用户提供了较好的用户体验.针对用户检索需求随时间变化而不同这一问题,时间敏感查询词自动补全成为研究热点.时间敏感查询词补全在生成查询词补全候选列表时拟合多种时间因素,呈现出与传统查询词补全不同的特点.本文首先介绍了时间敏感查询词补全的定义和分类,然后从查询词时间敏感类型判断、补全候选词权值计算、候选词排序计算三个步骤分析了关键技术,最后对技术评价方法和技术未来发展难点与热点进行了总结和展望. Query auto-completion of search engines provides good experience for the users. With the user＇s search intention changing over time,time-sensitive query auto-completion（ TSQC） comes to be a research focus. Different from traditional query auto-completion,recommendation list of TSQC is made according to the attaching time features of the query words. First,the definition and classification of TSQC are introduced. Then the key steps of TSQC are presented and analyzed,which include type judgment of time-sensitive query,weight calculation of candidates and recommendation list ranking. Finally,technique evaluation and future development of TSQC are analyzed and summarized.
作者 田萱 张骁 孟祥光 陈志泊
机构地区 北京林业大学信息学院
出处 《电子学报》 EI CAS CSCD 北大核心 2015年第6期1160-1168,共9页 Acta Electronica Sinica
基金 中央高校基本科研业务费专项资金资助(No.YX2014-19)
关键词 时间敏感 查询词补全 信息检索 候选词权值计算 time-sensitive query auto-completion information retrieval candidates＇ weights calculation
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
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参考文献45

  • 1Dakka W, Gravano L, Ipeirotis PG. Answering general time- sensitive queries [ J ]. Knowledge and Data Engineering, IEEE Transactions on,2012;24(2) :220 - 235.
  • 2Fu C-L, Silver D. Time-sensitive Sampling for Spare Filtering [M]. Ontario, Canda: Springer,2004.551 - 553.
  • 3Shokouhi M, Radinsky K. Time-sensitive query auto-completion [A]. Proceedings of the 35th International ACM SIGIR Con- ference on Research and Development in Information Retrieval [C]. Portland: ACM,2012. 601 - 610.
  • 4Zhang R, Chang Y, Zheng Z, Metzler D, Nie J-y. Search result re-ranking by feedback control adjustment for time-sensitive query[ A]. Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume [ C]. Boulder: Association for Computational Linguistics, 2009. 165 - 168.
  • 5Efron M. Linear time series models for term weighting in infor- marion retrieval[ J] .Journal of the American Society for Infor- mation Science and Technology, 2010,61 ( 7 ) : 1299 - 1312.
  • 6Dong A, Zhang R, Kolari P, Bai J, Diaz F, Chang Y, et al. Time is of the essence: Improving recency ranking using twitter data[ A ]. Proceedings of the 19th International Conference on World Wide Web[ C]. Raleigh:ACM,2010. 331 - 340.
  • 7Radinsky K, Svore K, Dumais S, Teevan J, Bocharov A, Horvitz E. Modeling and predicting behavioral dynamics on the web[ A]. Proceedings of the 21st International Conference on World Wide Web[ C] .Portland:ACM,2012. 599 - 608.
  • 8Bar-Yossef Z, Kraus N. Context-sensitive query auto-comple- tion[ A ]. Proceedings of the 20th Interla_ational Conference on World Wide Web[ C]. Hyderabad:ACM,2011. 107 - 116.
  • 9Bast H, Weber I. Type less, find more: fast autocompletion search with a succinct index[A]. Proceedings of the 29th An- nual International ACM SIGIR Conference on Research and Development in Information Retrieval [ C ]. Seattle: ACM, 2006. 364 - 371.
  • 10Chaudhuri S, Kaushik R. Extending autocompletion to tolerate errorsL A]. Proceedings of the 35th SIGMOD International Conference on Management of Data[ C ]. Providence: ACM, 2009.707 - 718.

二级参考文献169

  • 1Shardanand U, Maes P. Social information filtering: Algorithms for automating "Word of Mouth". In: Proc. of the Conf. on Human Factors in Computing Systems. New York: ACM Press, 1995.210-217.
  • 2Hill W, Stead L, Rosenstein M, Furnas G. Recommending and evaluating choices in a virtual community of use. In: Proc. of the Conf. on Human Factors in Computing Systems. New York: ACM Press, 1995. 194-201.
  • 3Resnick P, Iakovou N, Sushak M, Bergstrom P, Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. In: Proc. of the Computer Supported Cooperative Work Conf. New York: ACM Press, 1994. 175-186.
  • 4Baeza-Yates R, Ribeiro-Neto B. Modern Information Retrieval. New York: Addison-Wesley Publishing Co., 1999.
  • 5Murthi BPS, Sarkar S. The role of the management sciences in research on personalization. Management Science, 2003,49(10): 1344-1362.
  • 6Smith SM, Swinyard WR. Introduction to marketing models. 1999. http://marketing.byu.edu/htmlpages/courses/693r/modelsbook/ preface.html
  • 7Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 2005,17(6):734-749.
  • 8Resnick P, Varian HR. Recommender systems. Communications of the ACM, 1997,40(3):56-58.
  • 9Balabanovic M, Shoham Y. Fab: Content-Based, collaborative recommendation. Communications of the ACM, 1997,40(3):66-72.
  • 10Schafer JB, Konstan J, Riedl J. Recommender systems in e-commerce. In: Proc. of the 1 st ACM Conf. on Electronic Commerce. New York: ACM Press, 1999. 158-166.

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电子学报
2015年 第6期

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