Implicit Interest Indicators

Implicit Interest Indicators

Mark Claypool, Phong Le, Makoto Waseda and David Brown

In Proceedings of ACM Intelligent User Interfaces Conference (IUI)
Santa Fe, New Mexico, USA
January 14-17, 2001
Winner! Best paper award.
Winner! Most Impact Award. ACM Intelligent User Interfaces Conference, Greenville, SC, USA, 2024. (The "Most Impact Award" is given to papers previously published at the conference which have significantly influenced research or industry practices.)

Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can "intelligently" determine the interest of a user and use this information to make suggestions. The common solution, "explicit ratings", where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more "intelligent" method is to use implicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.

Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages.

Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.


Most Impact Award (2024):