Creating a scientific bibliography on a given topic is currently a task which requires a great deal of manual effort. We attempt to reduce this effort by developing a tool for automatically generating a bibliography from a collection of articles represented in XML. We evaluate the use of elements around the references as anchortexts to improve search results. We find that users of the tool prefer lists generated using anchortext over those generated from the bibliography entry only and that the preference is statistically significant. We tentatively find no significant preference for results generated using paragraph as opposed to sentence level anchortext, but note that this finding may result from lack of sophistication in resolving text including multiple references.

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There are significant barriers to academic research into user Web search preferences. Academic researchers are unable to manipulate the results shown by a major search engine to users and would have no access to the interaction data collected by the engine. Our initial approach to overcoming this was to ask participants to submit queries to an experimental search engine rather than their usual search tool. Over several different experiments we found that initial user buy-in was high but that people quickly drifted back to their old habits and stopped contributing data. Here, we report our investigation of possible reasons why this occurs. An alternative approach is exemplified by the Lemur browser toolbar, which allows local collection of user interaction data from search engine sessions, but does not allow result pages to be modified. We will demonstrate a new Firefox toolbar that we have developed to support experiments in which search results may be arbitrarily manipulated. Using our toolbar, academics can set up the experiments they want to conduct, while collecting (subject to human experimentation guidelines) queries, clicks and dwell times as well as optional explicit judgments.

This dissertation would have not been possible without the persistent guidance and encouragement of my mentors. I owe a big ‘thank you ’ to my advisor, Jamie Callan. It was during the Fall of 2004, when I took two half-semester courses taught by Jamie (Digital Libraries and Text Data Mining), that I discovered the field of Information Retrieval. Seven years later, I am writing my dissertation acknowledgments. Jamie’s advice on both research and life in general has been a great source of insight and support. His feedback on research papers, presentations, lecture slides, proposals, and this dissertation has taught me a great deal about communicating effectively. From Jamie’s example, I have learned valuable lessons on research, teaching, and mentoring. I would like to thank Jaime Carbonell, Yiming Yang, and Fernando Diaz for agreeing to be in my thesis committee. Their feedback was critical in making this dissertation stronger. Fernando Diaz helped shape many of the ideas presented in this dissertation. It was during an internship with Fernando at Yahoo! where I began working on vertical selection. I enjoyed it so much I returned for a second internship a year later. I have been fortunate to have had Fernando as a mentor and collaborator ever since.