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Abstract

A recommendation service is disclosed which uses collaborative filtering techniques to recommend books to users of a Web site. The Web site includes a catalog of the various titles that can be purchased via the site. The recommendation service includes a database of titles that have previously been rated and that can therefore be recommended by the service using collaborative filtering methods. At least initially, the titles and title categories (genres) that are included within this database (and thus included within the service) are respective subsets of the titles and categories included within the catalog. As users browse the site to read about the various titles contained within the catalog, the users are presented with the option of rating specific titles, including titles that are not currently included within the service. The ratings information obtained from this process is used to automatically add new titles and categories to the service. The breadth of categories and titles covered by the service thus grows automatically over time, without the need for system administrators to manually collect and input ratings data. To establish profiles for new users of the service, the service presents new users with a startup list of titles, and asks the new users to rate a certain number of titles on the list. To increase the likelihood that new users will be familiar with these titles, the service automatically generates the startup list by identifying the titles that are currently the most popular, such as the titles that have been rated the most over the preceding week.

First Claim