Inferring User Interest

Inferring User Interest

Mark Claypool, David Brown, Phong Le, Makoto Waseda

IEEE Internet Computing
November/December 2001

Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can 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 fairly well-understood and precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A less intrusive method is to use implicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. This research studies the relationship between various implicit ratings and the explicit rating for a single Web page, and the impact of implicit interest indicators on user privacy. We developed a Web browser that records a user's actions (implicit ratings) and the explicit rating for each page visited. The browser was used by over 70 people that browsed more than 2000 Web pages. We find that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling has a strong positive relationship with explicit interest, while individual scrolling methods and mouse-clicks are ineffective in predicting explicit interest.


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