HYBRID RECOMMENDER SYSTEMS SURVEY AND EXPERIMENTS 343 based"filterbots using specific criteria, such as the number of spelling errors and the size of included messages. These bots contributed ratings to the database of ratings used by the colla borative part of the system, acting as artificial users. With fairly simple agent implementations, they were able to improve email filterin Augmentation is attractive because it offers a way to improve the performance of a ore system, like the NetPerceptions' GroupLens Recommendation Engine or a ive Bayes text classifier, without modifying it. Additional functionality is added by intermediaries who can use other techniques to augment the data itself. Note that this is different from feature combination in which raw data from different sources is combined While both the cascade and augmentation techniques sequence two recom menders, with the first recommender having an influence over the second, they are fundamentally quite different. In an augmentation hybrid, the features used by the second recommender include the output of the first one, such as the ratings contributed by GroupLens'filterbots. In a cascaded hybrid, the second recom- mender does not use any output from the first recommender in producing its rankings, but the results of the two recommenders are combined in a prioritized manner 3.7. META-LEVEL Another way that two recommendation techniques can be combined is by using the model generated by one as the input for another. This differs from feature augmentation: in an augmentation hybrid, we use a learned model to generate fea- tures for input to a second algorithm; in a meta-level hybrid the entire model becomes the input. The first meta-level hybrid was the web filtering system Fab (Balabanovic 1997, 1998). In Fab, user-specific selection agents perform con- tent-based filtering using Rocchio,s method(Rocchio 1971) to maintain a term vector model that describes the user's area of interest. Collection agents which garner new pages from the web, use the models from all users in their gathering operations.So,documents are first collected on the basis of their interest to the community as a whole and then distributed to particular users. In addition to he way that user models were shared, Fab was also performing a cascade of col- laborative collection and content-based recommendation, although the collabor- ative step only created a pool of documents and its ranking information was not used by the selection component. A meta-level hybrid that focuses exclusively on recommendation is described by Pazzani(1999) as'collaboration via content. A content-based model is built by innow(Littlestone Warmuth, 1994)for eac describing the features that predict restaurants the user likes. These models, essentially vectors of terms and thts. can then be across users to make predie M Condliff et al. (1999) have used a two-stage Bayesian mixed-effects scheme: a con- tent-based naive Bayes classifier is built for each user and then the parameters Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission