342 ROBIN BURKE Ripper was applied to the task of recommending movies using both user ratings and content features, and achieved significant improvements in precision over a purely collaborative approach. However, this benefit was only achieved by hand-filtering content features. The authors found that employing all of the available content fea- tures improved recall but not precision The feature combination hybrid lets the system consider collaborative data without relying on it exclusively, so it reduces the sensitivity of the system to the number of users who have rated an item. Conversely, it lets the system have information about the inherent similarity of items that are otherwise opaque to a collaborative system 3.5. CASCADE Unlike the previous hybridization methods, the cascade hybrid involves a staged process. In this technique, one recommendation technique is employed first to pro- duce a coarse ranking of candidates and a second technique refines the recommen- dation from among the candidate set. The restaurant recommender EntreeC, described below, is a cascaded knowledge-based and collaborative recommender Like Entree, it uses its knowledge of restaurants to make recommendations based on the user's stated interests. The recommendations are placed in buckets of equal preference, and the collaborative technique is employed to break ties, further rank ing the suggestions in each bucket Cascading allows the system to avoid employing the second, lower-priority, tech ue on items that are already well-differentiated by the first or that are sufficiently poorly-rated that they will never be recommended Because the cascade's second step ocuses only on those items for which additional discrimination is needed, it is more efficient than, for example, a weighted hybrid that applies all of its techniques to all items. In addition, the cascade is by its nature tolerant of noise in the operation of a low-priority technique, since ratings given by the high-priority recommender can only be refined, not overturned 36. FEATURE AUGMENTATION One technique is employed to produce a rating or classification of an item and that information is then incorporated into the processing of the next recommendation technique. For example, the Libra system(Mooney roy, 1999) makes con- tent-based recommendations of books based on data found in Amazon. com, using a naive Bayes text classifier. In the text data used by the system is included related authors'and related titles' information that Amazon generates using its internal collaborative systems. These features were found to make a significant contribution to the quality of recommendations The GroupLens research team working with Usenet news filtering also employed feature augmentation(Sarwar et al., 1998). They implemented a set of knowledge- Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission