New Learning Frameworks for Information Retrieval


March 23, 2010


Yisong Yue


Cornell University


Information retrieval and access have become central technologies for managing and leveraging the ongoing explosion of digital content. While effective, current techniques for designing retrieval models are limited by two issues. First, they have restricted representational power, and generally deal with simple settings that estimate the quality of individual results independently of other results. Second, existing methodologies for designing retrieval functions are labor intensive and cannot be efficiently applied to accommodate a growing variety of retrieval domains.

In this talk, I will describe two learning approaches for designing new retrieval models. The first is a structured prediction approach, which considers inter-dependencies between results in order to optimize for more sophisticated objectives such as information diversity. The second is an interactive learning approach, which reduces the efficiency bottleneck of relying on human experts by leveraging data gathered from online user interactions; such data is both cheap to collect as well as naturally representative of user utilities in the target domain.

This is joint work with Thorsten Joachims.


Yisong Yue

Yisong Yue is a Ph.D. candidate at Cornell University, where he works on machine learning approaches to structured prediction and interactive systems, with an application focus in information retrieval. He is the author of the SVM-map software package for optimizing mean average precision using support vector machines, and he currently manages the experimental search service for the Physics E-Print ArXiv. He is also the recipient of a Microsoft Research Graduate Fellowship and a Yahoo! Key Scientific Challenges Award. His recent research focuses on machine learning approaches to learning from user interactions (implicit feedback), online experiment design, diversified retrieval, and interactive search.