344 ROBIN BURKE of the classifiers are linked across different users using regression. LaboUr(Schwab, et al., 2001)uses instance-based learning to create content-based user profiles which are then compared in a collaborative manner The benefit of the meta-level method, especially for the content/colla borative hybrid is that the learned model is a compressed representation of a user's interest and a collaborative mechanism that follows can operate on this information-dense epresentation more easily than on raw rating data 3.8. SUMMARY Hybridization can alleviate some of the problems associated with collaborative filtering and other recommendation techniques. Content/collaborative hybrids regardless of type, will always demonstrate the ramp-up problem since both tech niques need a database of ratings. Still, such hybrids are popular, because in many situations such ratings already exist or can be inferred from data. Meta techniques avoid the problem of sparsity by compressing ratings over many examples into a model, which can be more easily compared across users. Knowledge-based and utility-based techniques seem to be good candidates for hybridization since they are not subject to ramp-up problems. Table IV summarizes some of the most prominent research in hybrid recommender systems. For the sake of simplicity, the table combines knowledge- based and utility-based techniques (since utility-based recommendation is a special case of knowledge-based ). 4 There are four hybridization techniques that are order-insensitive: Weighted Mixed, Switching and Feature Combination. with these hybrids, it does not make sense to talk about the order in which the techniques are applied: a CN/CF mixed system would be no different from a CF/CN one. The redundant combinations are marked in gray The cascade, augmentation and meta- level hybrids are inherently ordered. For example, a feature augmentation hybrid that used a content-based recommender to contribute features to be used by a second collaborative process, would be quite ifferent from one that used collaboration first. To see the difference. consider the example of news filtering: the former case, content-based/ colla borative, would correspond to a learning content-based version of the GroupLens ' idea The latter arrangement, collaborative/content-based, could be implemented as a collaborative system that assigns users to a clique or cluster of similar users and then uses the clique ids as input to a content-based system, using these identifiers as well as terms from the news articles to produce the final recommendation We would expect these systems to have quite different characteristics. With cascade, re hybrids that combine techniques of the same type, although some do exist. PickA- Flick( Burke et al. 1997), for example, is a knowledge-based/knowledge-based mixed hybrid com- bining two different knowledge-based strategies. Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission