diampoxdin dia是什么意思思

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张敏
算机科学与技术系,&#2=
1103;教授
入职时间:2003年
电子邮件
电话
传真
教育背景
工学学士 (计算机科&#=
23398;与技术), 清华大&#=
23398;, 中国, 1999;
工学博士 (计算机科&#=
23398;与技术), 清华大&#=
23398;, 中国, 2003.
研究领域
信息检索, 用户行为&#=
20998;析, 机器学&#=
讲授课程
机器学习概论 (课号, 本科);
信息检索前沿&#3074=
0;究 (课号, 研究生).
教学概况
我所讲授的本&#3118=
5;生课程“机器学习概&#=
35770;”是计算机专业选&#2046=
2;课程,为学生今后从&=
#20107;人工智能、智能信&#24=
687;处理等方向的深入&#23398=
;习和研究打下相应的&#=
29702;论与实践基础。除&#201=
02;在课堂上讲授机器学=
习领域的经典算法以&#2=
2806;,我在每节课专设&#3575=
2;论环节,针对具体应&=
#29992;问题引导学生进行&#23=
454;验设计、特征分析&#21644=
;算法选择的讨论;此&#=
22806;,我通过两个课程&#234=
54;验,锻炼学生选择和=
设计合适算法解决具&#2=
0307;问题的能力以及数&#2545=
4;结果分析能力。在课&=
#31243;中,我注重启发学&#29=
983;的积极思考与分析&#65292=
;并着重培养学生形成&#=
33391;好的科学研究习惯&#122=
90;课程讲授受到了学生=
的好评,并获得了“&#28=
165;华大学青年教师教&#23398=
;优秀奖”。
我讲授的研究&#2998=
3;课程“信息检索前沿&#=
30740;究”,为“前沿研&#31350=
;”型课程。信息检索&#2=
6159;一个研究与产业紧&#2349=
4;结合、相互促进发展&=
#30340;领域,因此我在课&#31=
243;内容设计上,突出&#30740=
;究的前沿性和应用的&#=
21069;沿性。课程从学术&#307=
40;究领域中挑选出正在=
或者即将成为关注点&#3=
0340;、有重要意义的研&#3135=
0;方向,形成十个左右&=
#30340;专题,讲授最新的&#27=
169;型方法,并分析讨&#35770=
;该领域的开放性和挑&#=
25112;性问题。此外,我&#223=
12;每次课上都选取一周=
以来在产业界发生的&#1=
9982;信息检索技术和公&#2149=
6;相关的一两个热点话&=
#39064;展开讨论,帮助学&#29=
983;建立研究与应用的&#32852=
;系,培养学生的专业&#=
25935;感性和对新的产业&#211=
60;态的跟踪与把握能力=
。课程最后的四周时&#3=
8388;组织一个课内学术&#2013=
2;流会,模拟国际学术&=
#20250;议形式,以分组形&#24=
335;进行课题调研口头&#25253=
;告,并接受教师和学&#=
29983;的提问,达到了较&#229=
09;的教学效果,也得到=
了学生们的好评。
研究课题
国家自然科学&#2252=
2;金重点课题: 下一代信息检&#3203=
国家自然科学&#2252=
2;金: 主题无关的高&#3613=
6;量WEB页面预选与检&#3203=
教育部课题: 国家精品课程&#2044=
9;息检索系统
“清华-搜狐”联合研&#31350=
;室项目: 搜索引擎改进&#2145=
0;用户行为分析 (20=
微软亚洲研=
究院合作项目:基于&#2=
8857;击数据的搜索引擎&#3578=
0;价 (20=
研究概况
我的研究兴趣集中&#=
22312;网络信息检索模型&#214=
50;用户行为分析。在大=
多数信息检索任务中&#6=
5292;用户的需求描述模&#3194=
6;而简短,而文档数据&=
#30340;信息描述空间则非&#24=
120;大且内容复杂。因&#27492=
;,信息检索中的最大&#=
38382;题就是用户查询空&#383=
88;描述的信息与已知文=
档空间信息表示的不&#2=
1305;配。我的研究工作&#2360=
1;是围绕如何有效理解&=
#29992;户需求,挖掘用户&#34=
892;为特征,从网络数&#25454=
;中筛选高质量的信息&#=
65292;改进检索模型展开&#652=
92;力图减小用户需求与=
信息资源之间的不匹&#3=
7197;问题,从而改进信&#2468=
7;检索效果。基于这一&=
#24605;路,我们在国家自&#28=
982;科学基金重点项目&#12289=
;青年项目、与“清华&#8=
212;搜狐”搜索技术联合=
实验室的支持下开展&#2=
0102;一系列相关研究,&#2002=
7;要研究成果包括:
在信息检索模型与&#=
26041;法的研究方面,我&#235=
45;新信息检索的本质—&=
#8212;如何针对用户的检&#320=
34;需求,准确地找到包=
含新内容的非冗余信&#2=
4687;——进行研究,提出=
了基于查询扩展的文&#2=
6723;重构方法、新信息&#2659=
7;找中的查询扩展方法&=
#12289;基于选择池的有效&#20=
449;息匹配算法;从文&#26723=
;空间的信息表示的角&#=
24230;,我提出以目标为&#200=
13;心的文档信息重组方=
法、话题归并与重组&#2=
6041;法;针对观点检索&#1998=
2;评论倾向性分析问题&=
#65292;我提出将文档的相&#20=
851;性与观点倾向性相&#34701=
;合的产生式模型,将&#=
20108;者放在统一的框架&#200=
13;进行分析。 相=
关工作分别发表在JIR、SIGIR、CIKM等=
国际期刊和重要国际&#2=
3398;术会议上,并获得&#2226=
9;家发明专利授权1项。
此外,从用户角度&#=
20986;发,我们利用搜索&#243=
41;擎海量规模的用户行=
为数据信息,发挥“&#29=
992;户群体智慧”的作用=
, 提=
出了一系列基于用户&#3=
4892;为分析的网络信息&#2681=
6;索性能改进方法。基&=
#20110;用户行为分析的互&#32=
852;网页面质量评估及&#22403=
;圾网页识别方法,能&#=
22815;有效地解决垃圾页&#387=
54;识别的普适性和时效=
性;我提出的搜索引&#2=
5806;性能的自动评价模&#2241=
1;,能够解决传统信息&=
#26816;索评价方法中评价&#38=
598;合规模小、更新慢&#12289=
;需要大量人工标注、&#=
23454;时性差等问题,目&#210=
69;已提供中文主流搜索=
引擎性能评价的每天&#2=
2312;线服务;我从用户&#3528=
2;度理解用户查询需求&=
#65292;建立用户浏览图模&#22=
411;,评价用户检索结&#26524=
;的满意度;我同时还&#=
25552;出了用户行为的可&#387=
52;性分析方法,包括点=
击的可靠性分析算法&#1=
2289;用户级别的可靠性&#3578=
0;估算法,能够同时对&=
#39640;频查询及稀有查询&#30=
340;可靠性进行有效判&#26029=
;。相关成果发表在JASIST、WWW、CIKM、=
WSDM等相关研究领域国&#=
38469;著名期刊与会议上&#652=
92;并申请国家发明专利=
8项,其中已获得4项授权。我的研究&#=
25104;果同时通过“清华&#8212=
;搜狐”搜索技术联合&#2=
3454;验室的校企合作平&#2148=
8;应用到搜狗搜索引擎&=
#20013;,取得了良好的实&#38=
469;应用效果。
我的部分研究成果&#=
23637;示参见:中文搜索&#243=
41;擎性能自动评价系统=
——搜索仪平台(/)、热点新闻聚类&#=
23454;时服务系统(http://news.thuir.org)、搜狗实验室平&#=
21488;(/labs)。
管理与服&#21=
“清华—搜狐”&=
#25628;索技术联合实验室: 副主任 (2007-);
Information
Processing and Management (IP&M): &#=
23457;稿人(200=
,KDD 2010, WWW , EMNLP 2009, IJCNLP 2008: 程序委员会委&#2159=
程序委员会委&#2159=
2;、分领域共主席
2010: 程序委员=
会共主席 (20=
奖励与荣&#35=
清华大学青年&#25945=
;师教学优秀奖 (2007).
代表性论&#33=
[1] Min Zhang,
Xingyao Ye, A generative model to unify topic relevance and lexicon-based
sentiment for opinion retrieval, The 31st Annual International ACM SIGIR
Conference (SIGIR2008), 20-24 July 2008, Singapore, p411-419.
[2] Canhui Wan=
Min Zhang, Shaoping ma, Liyun Ru, Automatic Online News Issue Construction =
Web Environment, the 17th International World Wide Web Conference (WWW2008),
Beijing, April, 2008, p457-466.
[3] Yiqun Liu,=
Zhang, Liyun Ru, Shaoping Ma. Data Cleansing for Web Information Retrieval
using Query Independent Features. Journal of the American Society for
Information Science and Technology (JASIST), Volume 58, No. 12, Pages
[4] Le Zhao, M=
Zhang, Shaoping Ma, The Nature of Novelty Detection, Information Retrieval,=
9, No. 5, pp.521-542, 2006.&
[5] Rongwei Ce=
Yiqun Liu,Min Zhang, Bo Zhou, Liyun Ru, and Shaoping Ma. Exploring Relevance
for Clicks. In Proceeding of the 18th ACM Conference on information and
Knowledge Management. (CIKM 2009), Nov. 2009. ACM, New York, NY,
[6] Yiqun Liu,
Yijiang Jin, Min Zhang, Shaoping Ma and Liyun Ru. User Browsing Graph:
Structure, Evolution and Application. Late breaking result session in Second
ACM International Conference on Web Search and Data Mining (WSDM 2009). 200=
[7] Canhui Wan=
Min Zhang, Liyun Ru, Shaoping Ma, Automatic Online News Topic Ranking Using
Media Focus and User Attention Based on Aging Theory, the ACM 17th Conferen=
on Information and Knowledge Management (CIKM 2008), October, 2008, Napa Va=
California, USA. pp
[8] 张敏,宋睿华,马&#23569=
;平, 基于语&#=
20041;关系查询扩展的文&#267=
23;重构方法, 计算机&#=
23398;报,第27卷,第10期,, 2004
[9] Qing Ma, M=
Zhang, Ming Zhou, Masaki Murata, and Hitoshi Isahara, Self-Organizing Chine=
and Japanese Semantic Maps, International Conference on Computational
Linguistics (COLING02). p1-7, August, 2002.Taiwan.
[10]Jianfeng G=
Min Zhang, Improving Language Model Size Reduction using Better Pruning
Criteria. the Association for Computational Linguistics 40th Anniversary
Meeting, 2002 (ACL2001). p176-182, July, 2001
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