Social and content-based information filtering for a Web graphics recommender system

Existing social or content-based approaches to filtering-by-example are difficult to apply to image data. To realize a filtering-by-example system for image data, we propose a new approach to combine social and content-based filtering techniques. A content-based sub-system provides two types of clusters, equivalent items and virtual users, to overcome a disadvantage of social filtering, that is, a shortage of ratings. Since items similar in visual properties are not always similar in user tastes, a social sub-system controls the content-based sub-system with an evaluation function that estimates the validity of content-based clusters according to user ratings. Based on this approach, we have developed an image database, Web Graphics Navigator, that recommends graphics for Web pages according to the users' tastes. The database has been open to the public on the World-Wide Web to obtain user ratings. A preliminary observation of the user data shows promising results.