- VP of Yahoo! Research
- Research Professor, Carnegie Mellon University
- Executive Vice Chancellor The University of Kansas
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Talk Info: Abstract: In the online world, user engagement refers to the quality of the user experience that emphasizes the positive aspects of the interaction with a web application and, in particular, the phenomena associated with wanting to use that application longer and frequently. This definition is motivated by the observation that successful web applications are not just used, but they are engaged with. Users invest time, attention, and emotion into them. User engagement is measured in many ways, through methods of self-reporting (e.g., questionnaires), observer methods (e.g., facial expression analysis, speech analysis, desktop actions, etc.), neuro-physiological signal processing methods (e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms, etc.), and from a web analytics perspective (through online behavior metrics that assess users' depth of engagement with a site). These methods represent various tradeoffs between scale of data and depth of understanding (for instance, surveys are small-scale but deep, whereas clicks are large-scale but shallow in understanding). Little work has been done to integrate these various measures into a coherent understanding of engagement success. Online providers aim not only to engage users with each service, but across all services in their network. They spend increasing effort to direct users to various services (e.g.~using hyperlinks to help users navigate to and explore other services); in other words, to increase user traffic between their services. Nothing is known for users engaging across such a network of Web sites, something we call networked user engagement. We address this problem by combining techniques from web analytics and mining, information retrieval evaluation, and existing works on user engagement coming from the domains of information science, multimodal human computer interaction and cognitive psychology. In this way, we can combine insights from big data with deep analysis of human behavior in the lab or through crowd-sourcing experiment. This way of thinking is crucial to many areas, going beyond the web and will in time lead to a new genre of computational social sciences that transcend specific applications on the internet. This talk comprises three parts: (1) First we define user engagement, list its many characteristics as identified in the research and analytic literature, and discuss through real examples the challenges associated with measuring user engagement. (2) Second we describe recent data-driven approaches looking at user engagement through the development of new measures that allow for a better representation of how users engage with and across different web services, what we call networked user engagement (3) Finally we will describe how emerging research directions looking at affect and cognition as well as graph related measures are providing additional insights into measuring networked user engagement This work is being done in collaboration with Mounia Lalmas, Janette Lehmann, and Georges Dupret from Yahoo! Labs as well as Elad Yom-Tov (Microsoft Research). Bio: Ricardo Baeza-Yates is VP of Yahoo! Research for Europe, Middle East and Latin America, leading the labs at Barcelona, Spain and Santiago, Chile, since 2006, as well as supervising the lab in Haifa, Israel since 2008. He is also part time Professor at the Dept. of Information and Communication Technologies of the Universitat Pompeu Fabra in Barcelona, Spain, since 2005. Until 2005 he was Professor and Director of the Center for Web Research at the Department of Computer Science of the Engineering School of the University of Chile. He obtained a Ph.D. from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electrical engineering degree from the University of Chile, Santiago.He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, as well as co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 300 other publications. He has received the Organization of American States award for young researchers in exact sciences (1993) and the CLEI Latin American distinction for contributions to CS in the region (2009). In 2003 he was the first computer scientist to be elected to the Chilean Academy of Sciences. During 2007 he was awarded the Graham Medal for innovation in computing, given by the University of Waterloo to distinguished ex-alumni. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow. |
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Talk Info: Abstract: The scientific literature can be represented as a graph of documents, terms, and meta-data, with edges corresponding to containment of a term in a document, authorship of a document by a person, and so on. One popular way of querying such a graph is via queries based on proximity measures, such as Random Walk with Restart (RWR). In this talk, we describe a novel learnable proximity measure based on RWR. Instead of introducing one weight per edge label, as in most prior work, we introduce one weight for each edge label sequence. In this model proximity is defined by a weighted combination of simple "random walk experts", each corresponding to conducting a random walk constrained to follow a particular sequence of labeled edges. Experiments on eight tasks using graphs based on literature from two subdomains of biology show that the new learning method significantly outperforms the prior methods. We extend the method to support two additional types of experts to model intrinsic properties of entities: "query-independent experts", which generalize the PageRank measure, and "popular entity experts" which allow rankings to be adjusted for particular entities that are especially important. Finally, we present experiments in which we use this approach to learn relationships in the ontology of NELL, a wide-coverage, large-scale information extraction system for web data. We show that these types of learnable "proximity measures" are general enough to accurately model a significant number of real-world relations, and that they outperform an alternative technique that learns to model relations based on more traditional logical rules. Bio: William Cohen received his bachelor's degree in Computer Science from Duke University in 1984, and a PhD in Computer Science from Rutgers University in 1990. From 1990 to 2000 Dr. Cohen worked at AT&T Bell Labs and later AT&T Labs-Research, and from April 2000 to May 2002 Dr. Cohen worked at Whizbang Labs, a company specializing in extracting information from the web. Dr. Cohen is President of the International Machine Learning Society, an Action Editor for the Journal of Machine Learning Research, and an Action Editor for the journal ACM Transactions on Knowledge Discovery from Data. He is also an editor, with Ron Brachman, of the AI and Machine Learning series of books published by Morgan Claypool. In the past he has also served as an action editor for the journal Machine Learning, the journal Artificial Intelligence, and the Journal of Artificial Intelligence Research. He was General Chair for the 2008 International Machine Learning Conference, held July 6-9 at the University of Helsinki, in Finland; Program Co-Chair of the 2006 International Machine Learning Conference; and Co-Chair of the 1994 International Machine Learning Conference. Dr. Cohen was also the co-Chair for the 3rd Int'l AAAI Conference on Weblogs and Social Media, which was held May 17-20, 2009 in San Jose, and is the co-Program Chair for the 4rd Int'l AAAI Conference on Weblogs and Social Media, which will be held May 23-26 at George Washington University in Washington, D. C. He is a AAAI Fellow, and in 2008, he won the SIGMOD "Test of Time" Award for the most influential SIGMOD paper of 1998. Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 100 publications. |
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Talk Info: Abstract: We describe recent breakthroughs in the field of compressed data structures, in which the data structure is stored in a compressed representation that still allows fast answers to queries. We focus in particular on compressed data structures to support the important application of pattern matching on massive document collections. Given an arbitrary query pattern in textual form, the job of the data structure is to report all the locations where the pattern appears. Another variant is to report all the documents that contain at least one instance of the pattern. We are particularly interested in reporting only the most relevant documents, using a variety of notions of relevance. We discuss recently developed techniques that support fast search in these contexts as well as under additional positional and temporal constraints. Bio: Dr. Jeffrey Vitter (M.B.A., Duke University, 2002; Ph.D., Stanford University, 1980; B.S. with highest honors, University of Notre Dame, 1977) is the provost and executive vice chancellor and the Roy A. Roberts Distinguished Professor at the University of Kansas. Previously he was on the faculty at Texas A&M University, where from 2008-2009 he served as provost and executive vice president for academics, with additional responsibilities for the academic mission of Texas A&M University in Doha, Qatar. From 2002-2008, Dr. Vitter served as the Frederick L. Hovde Dean of the College of Science and Professor in the Department of Computer Science at Purdue University. From 1993-2002, Dr. Vitter held a distinguished professorship at Duke University, where he was the Gilbert, Louis, and Edward Lehrman Professor. He served at Duke as chair of the Department of Computer Science from 1993-2001 and as co-director and founding member of the Center for Geometric and Biological Computing. From 1980-1992, he progressed through the faculty ranks and served in leadership roles in the Department of Computer Science at Brown University. Dr. Vitter serves on the Board of Advisors for the School of Science and Engineering at Tulane University. From 2000-2009 Dr. Vitter served on the Board of Directors of the Computing Research Association (CRA), and he continues to co-chair its Government Affairs Committee. He chaired ACM SIGACT, the Special Interest Group on Algorithms and Computation Theory, of the world's largest computer professional society, the Association for Computing Machinery. Dr. Vitter is a Fellow of the Guggenheim Foundation, the American Association for the Advancement of Science, the Association for Computing Machinery, and the Institute of Electrical and Electronics Engineers. He was named a National Science Foundation Presidential Young Investigator and is a Fulbright Scholar. He has over 280 book, journal, conference, and patent publications, primarily on the algorithmic aspects of processing massive amounts of information. He is an ISI highly cited researcher with a Google Scholar h-index of 60. |
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