公司难得的学术交流机会

继上次丘成桐教授的演讲之后，本周四下午来自瑞士联邦理工学院洛桑分校（EPFL）和香港科技大学（HKUST）的几位专家又将为阿里巴巴的同学们带来3场精彩演讲。

演讲主题（全部是中文演讲）：
1. Impact of Recommenders on Consumer Behaviors (Pearl Pu浦还珠教授，瑞士联邦理工学院洛桑分校，Human Computer Interaction Group，
Faculty of Information and Communication Sciences)
2. Exploiting the Collective Wisdom in Recommendation Systems (Nathan Nan Liu，香港科技大学)
3. Transfer Learning with Applications (Qiang Yang杨强教授, 香港科技大学, IEEE Fellow)；

附：
1.演讲嘉宾及主题介绍
（主题简介）Impact of Recommenders on Consumer Behaviors (Pearl Pu浦还珠教授，瑞士联邦理工学院洛桑分校，Human Computer Interaction Group，Faculty of Information and Communication Sciences)
演讲嘉宾简介
Dr. Pearl Pu (浦还珠) is the director of the Human Computer Interaction Group at the Swiss Federal Institute of Technology in Lausanne (EPFL) where she teaches and conducts research in HCI and consumer decision behaviors. She has been recently elected as the general chair for the ACM international conference on Intelligent User Interfaces (IUI 2011) and ACM international conference on Recommender Systems (Recsys 2008), and program co-chair of the ACM international conference in Electronic Commerce (EC 2009) and Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008). A native from Shanghai, she moved to the United States shortly after passing the entrance examination to the ZheJiang University. She obtained her Master and Ph.D. degrees from the University of Pennsylvania in artificial intelligence and computer graphics. She was a visiting scholar at Stanford University in 2001 and more recently at HKUST in 2010. She has consulted many online companies on recommender system design, mobile interfaces, and personalized product search.
主题简介：
As online stores offer practically an infinite shelf space, recommender systems are playing an increasingly important role in helping users search and discover items that they may want to buy. In contrast to the proliferation of personalized Web services used in online industries and the widespread publication on the algorithmic success of recommenders, little is known about the effects of recommenders on consumer decision behaviors, for example, “which items should be recommended to influence consumers’ basket construction?” In this talk, I present some results based on empirical work that we have conducted in understanding and evaluating recommender systems impact on consumer behaviors.
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（主题二）Exploiting the Collective Wisdom in Recommendation Systems (Nathan Nan Liu，香港科技大学)
演讲嘉宾简介：
Liu Nan is a PHD candidate in the department of computer science and engineering at HKUST working with Qiang Yang. He current works focus on machine learning and data mining techniques with applications to recommendation systems. He has published papers at SIGIR, CIKM and WWW and has served as PC members in EMNLP'09, AAAI'10 and KDD'10 and a guest editor for IEEE Intelligent Systems Special Issue on Social Learning.
主题简介：
Collaborative filtering is a powerful technology for making recommendations based on the behaviors of massive number of users. It is well known that some of most successful internet services such as Amazon and Digg heavily rely on collaborative filtering to make recommendations to their users. In this talk, we will present an overview of several new research directions in the area of collaborative filtering research that is being pursued at HKUST. Firstly, we present ranking based models for collaborative filtering for directly optimizing the quality of top-k recommendation list. Secondly, we would discuss how to exploit implicit user feedback such as clicks, purchases in addition to the more traditional explicit user feedback normally in the form of ratings. Thirdly, we present distributed algorithms for scaling matrix factorization models to massive datasets. Finally, we show how to more accurately combine user behaviors observed multiple highly diverse domains via transfer learning.
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（主题三）Transfer Learning with Applications (Qiang Yang杨强教授, 香港科技大学, IEEE Fellow)
演讲嘉宾简介：
Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology and an IEEE Fellow. His research interests are artificial intelligence, including automated planning, machine learning and data mining. He graduated from Peking University in 1982 with BSc. in Astrophysics, and obtained MSc. degrees in Computer Science and in Astrophysics from the University of Maryland, College Park in 1985 and 1987, respectively, as well as his PhD in Computer Science from the University of Maryland, College Park in 1989.  He was an assistant/associate professor at the University of Waterloo between 1989 and 1995, and a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001.  He is a fellow of IEEE, and a member of AAAI, AAAS and ACM. He is an author of two books and over 200 publications on AI and data mining. His research teams won the 2004 and 2005 ACM KDDCUP international competitions on data mining.  He is an invited speaker at IJCAI 2009, ACL 2009 and ACML 2009.
Qiang Yang is on the editorial boards of several international journals.  He is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST).  He is on the editorial board of IEEE Intelligent Systems and Journal of Web Intelligence.  Previously he has been an associate editor for IEEE Transactions on Knowledge and Data Engineering, and Journal of Knowledge and Information Systems. He has been an organizer for several international conferences in AI and data mining, including the PC co-chair for ACM KDD 2010, the conference co-chair for ACM IUI 2010, Tutorial co-chair for AAAI 2005/2006, Workshop chair for ACM KDD 2007, program co-chair for PRICAI 2006 and PAKDD 2007, data mining contest chair for IEEE ICDM 2007/2009, vice chair for ICDM 2006 and CIKM 2009, conference chair for ICCBR 2001 and PC co-chair for Canadian AI conference in 2000. His home page is at http://www.cse.ust.hk/~qyang
主题简介：
Transfer learning is a new machine learning and data mining framework that allows the training and future data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. In this talk, I will give an introduction to transfer learning and then highlight some important applications such as text and image classification, sensor data mining and activity recognition, collaborative filtering and bioinformatics.  I will also discuss some potential future directions of transfer learning.