We found that there is an opportunity to achieve significant improvement by custom-tailoring search results to individuals, and were thus motivated to pursue search algorithms that return personalized results instead of treating all users the same.I like this excerpt that argues for implicit personalization:
People are typically unwilling to spend extra effort on specifying their intentions .... A promising approach to personalizing search results is to develop algorithms that infer intentions implicitly rather requiring that the user's intentions be explicitly specified.Unfortunately, the paper only explores keyword-based approaches that build a profile of keyword interests from past behavior and favor search results with those keywords. The paper doesn't cover collaborative filtering or social networking approaches that share data among users or the subject-based personalization used by sites like Google Personalized Search.
Susan Dumais and Eric Horvitz from Microsoft Research are co-authors on this paper. Susan and Eric have had some fun research work lately, including the Memex-inspired Stuff I've Seen project and the personalized news prototype NewsJunkie.
Thanks, Gary Price, for pointing out the recent SIGIR 2005 conference. A bunch of good papers this year, well worth a peek.
Update: I didn't realize it at first, but I think this Teevan et al. paper is about the same work as a project that was demoed at Microsoft TechFest earlier this year. The TechFest prototype uses the files on your desktop (Word files, Excel files, etc.) to personalize your web search. I'm still as skeptical now as I was then about that idea, but it's interesting to see more details in this SIGIR paper.
Update: Jon Gordon on NPR Future Tense interviewed Jaime Teevan today about her research work. [via Lee Odden]