III: Medium: Learning from Implicit Feedback Through Online Experimentation

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The goal of the project is to harness the information contained in users' interactions with information systems (e.g., query reformulations, clicks, dwell time) to train those systems to better serve their users' information needs. The key challenge lies in properly interpreting this implicit feedback and collecting it in a way that provides valid training data. Moving beyond existing passive data collection methods, the project draws on multi-armed bandit algorithms, experiment design, and machine learning to actively collect implicit feedback data. Developing these interactive experimentation methods goes hand-in-hand with developing machine learning algorithms that can use the resulting training data, and empirical evaluations that validate the models of user behavior assumed by the algorithms. This research will improve retrieval quality for important applications like intranet search and desktop search. Additionally, the project will provide an operational full-text search engine for the Physics E-Print ArXiv and potentially other digital libraries, thus forming a test-bed for the research while also providing a valuable service and dissemination tool to the academic community beyond computer science. The project provides interesting and motivating research opportunities to undergrads and international exchange students, and the PIs will include relevant material into the undergraduate and graduate curriculum. Finally, following their prior work on the Support Vector Machine, SVM-light (http://svmlight.joachims.org/) and an open-source search engine for learning ranked retrieval functions and evaluating the learned rankings, OSMOT (http://radlinski.org/osmot/), the PIs will continue to provide easy-to-use software that enables research and teaching, via the project website (http://www.cs.cornell.edu/People/tj/implicit/).