中国中文信息学会(CIPS)
中国计算机学会(CCF)
昆明理工大学
昆明理工大学智能信息处理重点实验室
 
 
特邀报告

 
报告人:杨强

 华为公司诺亚方舟实验室主任,香港科技大学计算机系教授。他是AAAI/IEEE/AAAS /IAPR Fellow和ACM杰出科学家。主要研究兴趣包括人工智能和数据挖掘。他于1982年获得北京大学天体物理专业学士学位。分别于1985年和1987年毕业于美国马里兰大学的计算机系和天文学系,获得双硕士学位。于1989年获得马里兰大学计算机系博士学位。自1989年到1995先后任滑铁卢大学的助理教授和副教授,1995年到2001年在加拿大Simon Fraser大学先后任副教授,教授,同期担任NSERC工业研究主任。自2001年,在香港科技大学先后任副教授,教授。他发表了300多篇关于人工智能和数据挖掘的论文,同时是清华大学出版社出版的《学术研究, 你的成功之路》一书的共同作者。在2004年和2005年,他指导的队伍赢得了KDDCUP等比赛的冠军。2010年7月, 他当选为ACM SIGART副主席。他是ACM TIST杂志的创始主编和IEEE Intelligent Systems编委。他也是多个人工智能和数据挖掘相关会议的组织者以及程序联合主席,如KDD 2012,KDD 2010,RecSys 2013,ACM IUI 2010等。他还是国际人工智能大会(IJCAI)的董事会成员,以及2015年在阿根廷举办IJCAI会议程序委员会主席。




报告题目:社交与移动网络中的用户建模
报告摘要: 日益增长的社交与移动网络为我们提供了大量的用户行为数据。基于这样的数据,我们可以建立各种用户模型,为用户提供更优质的服务。我将介绍华为诺亚方舟实验室的几项相关研究,包括社交网络上的用户建模,移动用户的行为建模,以及从社交网络数据到移动数据的迁移学习模型。同时,我也将介绍在大规模推荐模型等应用中的几个用户建模的实例。

报告人:刘铁岩

   刘铁岩,现任微软亚洲研究院高级研究员/主任研究员,领导互联网经济和计算广告学方面的研究工作。 他的研究兴趣包括:信息检索、机器学习、数据挖掘、算法博弈论等。他被公认为“排序学习”这一学术领域的代表人物, 在顶级国际会议和期刊(如SIGIR,WWW,KDD,ICML,NIPS,COLT,JMLR,FnTIR)上发表了数十篇有关排序学习的论文,已被引用五千余次,并受 Springer之邀撰写了排序学习领域的首部学术专著。近年来,他将机器学习和算法博弈论相结合,用以学习最优的经济机制,其研究成果发表在WWW,AAAI,IJCAI,EC, WINE,ACM TIST等顶级国际会议和期刊上。刘铁岩博士的研究工作多次获得最佳论文奖、最高引用论文奖、研究突破奖,并被CNet, ZDNet, Business Week, NPR等国际媒体广泛报道。他受邀担任了RIAO 2010、AIRS 2013、WINE 2014、ACML2015等国际会议的程序委员会主席,ICML 2014的组委会主席,WWW 2014和SIGIR 2016的讲座主席、WSDM 2015的博士论坛主席、KDD 2012的演示/展览主席, 并多次担任SIGIR、WWW、ACML、AIRS的领域主席。他现任ACM Transactions on Information Systems副主编,国际期刊Information Retrieval、Foundations and Trends in Information Retrieval编委。他是国际电子电气工程师学会(IEEE)、美国计算机学会(ACM)和中国计算机学会(CCF)的高级会员,中国科技大学、中山大学和南开大学的兼职教授/博导,英国诺丁汉大学的荣誉教授。




报告题目:计算广告学:研究前沿和技术挑战
报告摘要: 在线广告已经成为了互联网产业的基本盈利模型之一,受到了工业界和学术界的广泛关注。计算广告学,作为一门新兴学科, 旨在研究如何设计有效的算法和机制以促进在线广告的良性发展,它与信息检索,机器学习,算法博弈论,经济学等诸多领域密切相关。本报告将首先对在线广告和计算广告学进行基本介绍,描述在线广告生态环境中多方参与者(广告主、用户、出版商、广告平台)之间的复杂关系,并讨论计算广告学的基本研究问题:用户点击率预测、广告主策略建模、最优拍卖机制设计等等。其后,将介绍讲者在上述研究领域取得的最新研究成果:包括基于用户心理学提取有效特征进行点击率预测、使用递归深度神经网络来预测用户的时序点击行为、利用带有随机环境的马尔科夫过程对广告主的竞价行为进行建模、以及采用“博弈机器学习”的方法对拍卖机制进行优化。最后将会对计算广告学的未来发展和技术挑战进行探讨,以期激发听者对于这个新兴学科的研究兴趣。

报告人:翟成祥

    ChengXiang Zhai is a Professor of Computer Science and Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, where he is also affiliated with the Graduate School of Library and Information Science, Institute for Genomic Biology, and Department of Statistics. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and a Senior Research Scientist from 1997 to 2000. His research interests include information retrieval, text mining, natural language processing, machine learning, and biomedical informatics. He has published over 200 research papers with high citations. In particular, his paper on smoothing of language models for information retrieval is among the 10 most cited papers in information retrieval. He is an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and served on the editorial board of Information Retrieval Journal. He is a conference program co-chair of ACM CIKM 2004, NAACL HLT 2007, ACM SIGIR 2009, ECIR 2014, ICTIR 2015, and WWW 2015, and conference general co-chair for ACM CIKM 2016. He is an ACM Distinguished Scientist and a recipient of multiple best paper awards, Rose Award for Teaching Excellence at UIUC, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).




报告题目:Towards a Game-Theoretic Framework for Information Retrieval
报告摘要: The task of information retrieval (IR) has traditionally been defined as to rank a collection of documents in response to a query. While this definition has enabled most research progress in IR so far, it does not model accurately the actual retrieval task in a real IR application, where users tend to be engaged in an interactive process with multipe queries, and optimizing the overall performance of an IR system on an entire search session is far more important than its performance on an individual query.In this talk, I will present a new game-theoretic formulation of the IR problem where the key idea is to model information retrieval as a process of a search engine and a user playing a cooperative game, with a shared goal of satisfying the user's information need while minimizing the user's effort and the resource overhead on the retrieval system. Such a game-theoretic framework offers several benefits. First, it naturally suggests optimization of the overall utility of an interactive retrieval system over a whole search session, thus breaking the limitation of the traditional formulation that optimizes ranking of documents for a single query. Second, it models the interactions between users and a search engine, and thus can optimize the collaboration of a search engine and its users, maximizing the "combined intelligence" of a system and users. Finally, it can potentially serve as a unified framework for optimizing both interactive information retrieval and active relevance judgment acquisition through crowdsourcing. I will discuss how the new framework can not only cover several emerging directions in current IR research as special cases, but also open up many interesting new research directions in IR.

报告人:张奕

   Yi Zhang is an Associate Professor in School of Engineering at University of California Santa Cruz, with affiliation in Technology Management Department, Computer Science Department, Applied Math and Statistics Department, and Economics Department. Her research interests are recommendation systems, information retrieval, applied machine learning, natural language processing, and computational economics. She has received various awards, including ACM SIGIR Best Paper Award, National Science Foundation Faculty Career Award, Google Research Award, Microsoft Research Award, and IBM Research Fellowship. She has serve as program co-chair for IR in CIKM, area chair and PC member for various conferences such as SIGIR, WWW, SIGKDD, and ICML. She is an associate editor for ACM Transaction on Information Systems. She has served as a consultant or a technical adviser for several companies and startups. She received her B.S. from Department of Computer Science & Technology at Tsinghua University and her M.S. and Ph.D. from School of Computer Science at Carnegie Mellon University.




报告题目:Recommendation Systems: Machine Learning and Economics
报告摘要: A recommendation system acts in anticipation of the needs of a user and proactively pushes recommendation of the right product to the right people at the right time without requiring a user to issue an explicit query to a search engine. To do this, it explicitly models and learns user preferences while interacting with users, continuously monitors time varying candidate items and user context, and recommend the most relevant items to a specific user when appropriate. In this talk, we will present our work on combining Bayesian statistical machine learning and economics theories to help the software agent decide what to recommend, when to recommend and how to recommend. Experimental results in various domains (e-commerce, restaurants, etc.) will be presented and discussed.



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