Universal Web Search Relevance
Speaker: Belle Tseng , Yahoo!Contact:
Date: March 3 2009
Time: 11:00AM to 12:00PM
Location: Star Seminar Room 32-D463
Host: Rob Miller, MIT CSAIL
Michael Bernstein, (617) 253-0452, firstname.lastname@example.orgRelevant URL:
With the fast penetration of the Web throughout the world, the number of search users has increased dramatically from many geographic locations. Search engines are now facing the problem of providing search results to many countries. Machine Learned Ranking (MLR) approach has shown successes in web search. With the increasing demand to develop effective ranking functions for many countries (domains), we face a big bottleneck of insufficient training data to build a learned ranker for each domain.
In my talk, I will present two approaches to resolve this problem.
The first is a tree-based adaptation that takes a ranking function from one domain and tunes it with a small amount of training data from the target domain. The second approach is a Dynamic Bayesian Network click model that combines small amounts of training data with click data to build an unbiased estimation of the search relevance. Finally, I will report our experiments in evaluating the two approaches on a large dataset from the Yahoo! Search query logs, and report our findings.
Dr. Belle Tseng is a Senior Manager in the Web Search Ranking Department of Yahoo. She leads a R&D team of researchers with strong background in information retrieval and machine learning to improve the search relevance of Yahoo search engines across the world. Belle is an alumnus of MIT receiving her B.S. and M.S. in Mathematics and Electrical Engineering from MIT, and her Ph.D. from Columbia University. Before joining Yahoo, Belle spent four years as a Senior Research Staff Member at NEC Laboratories America where she manages research projects on relational data mining and social network analysis. Prior to joining NEC, she spent seven years as a Research Staff Member at IBM T. J. Watson Research Center working on multimedia retrieval, personalization, and summarization.
Dr. Tseng published over 100 technical papers in the area of web search, multimedia understanding, stereoscopic system, and social information analysis. She is a receipt of the NSF Fellowship, the IBM Invention Achievement Awards, and the NEC Technology Commercialization Award.
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