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