[ChimeText] Yahoo! visit to NUS -- addendum

[Chua, Tat-Seng]

Please get school to advertise to bigger mailing list.

On Tue, 15 Jul 2008, Min-Yen Kan wrote:

> Hi all:
>
> The talks by the Yahoo! folks has been finalized.  Please reserve your
> time on next Friday to keep up to date with the great IR research
> coming out of Yahoo!
> Please publicize to all folks who you think might be interested.  We'd
> like as many folks to come out for this as possible!
>
> Cheers, Min
>
> Speakers: Yahoo! Research Lab Staff
> Venue: SR3 (COM1 02-12)
> Date: 25 Jul (Friday)
> Time: 9:30am-11:30am (questions till 12:00nn)
>
> Talk Overviews (times are approximate):
> 9:30-10:00 - Evgeniy Gabrilovich / Overview of Computational Advertising
> 10:00-10:30 - Rosie Jones / Geography in Web Search
> 10:30-11:00 - Donald Metzler / Predicting when (not) to Advertise
> 11:00-11:30 - Vanessa Murdock / Diversifying Image Search with User
> Generated Content
>
> 1) Evgeniy Gabrilovich
>
> Title: Overview of Computational Advertising
>
> Abstract: Web advertising is the primary driving force behind many Web
> activities, including Internet search as well as publishing of online
> content by third-party providers. A new discipline - Computational
> Advertising - has recently emerged, which studies the process of
> advertising on the Internet from a variety of angles. A successful
> advertising campaign should be relevant to the immediate user's
> information need as well as more generally to user's background, be
> economically worthwhile to the advertiser and the intermediaries (e.g.,
> the search engine), as well as not detrimental to user experience. At
> first approximation, the process of obtaining relevant ads can be
> reduced to conventional information retrieval, where one constructs a
> query that describes the user's context, and then executes this query
> against a large inverted index of ads. We show how to augment the
> standard IR approach using query expansion and text classification
> techniques. We demonstrate how to employ a relevance feedback assumption
> and use Web search results retrieved by the query. We will also survey
> the numerous challenges and open research problems posed by
> computational advertising, such as text summarization, natural language
> generation, named entity extraction, handling geographic names, and
> others.
>
> Bio: Evgeniy Gabrilovich is a Senior Research Scientist and Manager of the
> NLP & IR Group at Yahoo! Research. His research interests include
> information retrieval, machine learning, and computational linguistics.
> Recently, he co-organized a workshop on the synergy between Wikipedia
> and research in AI at AAAI 2008, as well as co-presented a tutorial on
> computation advertising at ACL 2008 and EC 2008. He served on the
> program committees of ACL-08:HLT, AAAI 2008, WWW 2008, CIKM 2008, JCDL
> 2008, AAAI 2007, EMNLP-CoNLL 2007, and COLING-ACL 2006. Evgeniy earned
> his MSc ad PhD degrees in Computer Science from the Technion - Israel
> Institute of Technology. In his Ph.D. thesis, Evgeniy developed a
> methodology for using large scale repositories of world knowledge (e.g.,
> all the knowledge available in Wikipedia) in order to enhance text
> representation beyond the bag of words. URL:
> http://research.yahoo.com/Evgeniy_Gabrilovich
>
> 2) Rosie Jones
>
> Title: Geography in Web Search
>
> Abstract: Web search results are typically based on the user's search query,
> without taking other contextual information into account. However, we
> can see from user search behavior that for some search topics the user
> may prefer results which are geographically close to home. We will show
> topics which have a geographical dependence, as well as others which
> appear to be geographically independent. Based on these findings, we
> propose a more flexible approach to web search, which in which we prefer
> a ranking with results close to the user location when this will best
> satisfy the user's information need.
>
> Bio: Rosie Jones is a Senior Research Scientist at Yahoo!. Her research
> interests include web search, geographic information retrieval and
> natural language processing. She received her PhD from the School of
> Computer Science at Carnegie Mellon University. In 2005 she co-organized
> the SIGIR workshop on lexical cohesion and information retrieval, and in
> 2003 she co-organized the ICML workshop on The Continuum from Labeled to
> Unlabeled Data in Machine Learning and Data Mining. She served as a
> Senior PC member for SIGIR in 2007 and 2008.  URL:
> http://research.yahoo.com/Rosie_Jones
>
> 3) Donald Metzler
>
> Title: Predicting when (not) to Advertise
>
> Abstract: In this talk we discuss the problem of whether or not to show online
> advertisements. We propose two methods for addressing this problem, a
> simple thresholding approach and a machine learning approach, which
> collectively analyzes the set of candidate ads augmented with external
> knowledge. Our experimental evaluation, based on over 28,000 editorial
> judgments, shows that we are able to predict, with high accuracy, when
> to show ads for both content match and sponsored search advertising
> tasks.
>
> Bio: Donald Metzler is a Research Scientist at Yahoo! Research in Santa
> Clara, CA. He obtained his Ph.D. degree in Computer Science from the
> University of Massachusetts Amherst in 2007. His research interests
> include information retrieval, machine learning, and their intersection.
> He is the co-author of Search Engines: Information Retrieval in
> Practice, which will be published in the early part of 2009.  URL:
> http://research.yahoo.com/Don_Metzler
>
> 4) Vanessa Murdock
>
> Title: Diversifying Image Search with User Generated Content
>
> Abstract: Large-scale image retrieval on the Web relies on the availability of
> short snippets of text associated with the image.  This user-generated
> content is a primary source of information about the content and context
> of an image. While traditional information retrieval models focus on
> finding the most relevant document without consideration for diversity,
> image search requires results that are both diverse and relevant.  This
> is problematic for images because they are represented very sparsely by
> text, and as with all user-generated content the text for a given image
> can be extremely noisy.
>
> The contribution of this paper is twofold. We show that it is possible
> to minimize the trade-off between precision and diversity, relevance
> models offer a unified framework to afford the greatest diversity
> without harming precision.  Furthermore we show that estimating the
> query model from the distribution of tags favors the dominant sense of a
> query.  Relevance models operating only on tags offers the highest level
> of diversity with no significant decrease in precision.
>
> Bio: Vanessa Murdock currently holds a Post Doc position at Yahoo! Research
> Barcelona. Her current work focuses on retrieval of short texts, such as
> for advertisements, and user-generated content for images and video. She
> completed her PhD in 2006 at the University of Massachusetts, working
> with W. Bruce Croft. Her thesis, focusing on sentence retrieval for
> applications such as Question Answering, novelty detection, and
> information provenance, was recently published as a book "Exploring
> Sentence Retrieval. URL: http://research.yahoo.com/Vanessa_Murdock
>
> Upcoming Talks:
> 16 Jul: Xiong Deyi (I2R / Linguistically Annotated BTG for Statistical
> Machine Translation)
> 17 Jul: Douglas Oard (University of Maryland / Fourth-Generation
> Content Analysis: Supporting social science research using
> computational linguistics)
> 18 Jul: (related seminars) 3 seminars on 1) Real-Time Document Image
> Retrieval with LLAH 2) Large-Scale and Real-Time Specific Object 3)
> Pattern recognition with supplementary information
> 25 Jul: Yahoo! Research Labs talks:
> 4 talks on 1) Evgeniy Gabrilovich / Overview of Computational
> Advertising 2) Rosie Jones / Geography in Web Search 3) Donald Metzler
> / Predicting when (not) to Advertise 4) Vanessa Murdock / Diversifying
> Image Search with User Generated
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====================================================================
   CHUA, Tat-Seng (Dr),  Professor
   Department of Computer Science (AS6, #05-08)
   School of Computing, National University of Singapore
   Computing 1, SINGAPORE 117590
   Tel:+(65) 6516-2505, Fax:+(65) 6779-4580, Email: chuats at comp.nus.edu.sg
   Web: http://www.comp.nus.edu.sg/~chuats