ROBIN BURKE Table Ill. Hybridization methods Hybridization method Description The scores(or votes) of several recommendation techniques are combined together to produce a single recommendation Switching The system switches between recommendation techniques depending the current situation Mixed Recommendations from several different recommenders are presented at the same time Feature combination Features from different recommendation data sources are thrown together into a single recommendation algorithm Cascade One recommender refines the recommendations given by another. Feature augmentation Output from one technique is used as an input feature to another. Meta-level The model learned by one recommender is used as input to another perform post-hoc credit assignment and adjust the hybrid accordingly. However, the implicit assumption in this technique is that the relative value of the different tech- niques is more or less uniform across the space of possible items From the discussion above, we know that this is not always so: a collaborative recommender will be weaker for those items with a small number of raters 3.2. SWITCHING A switching hybrid builds in item-level sensitivity to the hy bridization strategy: the stem uses some criterion to switch between recommendation techniques. The Daily Learner system uses a content/ collaborative hybrid in which a content-based recommendation method is employed first. If the content-based system cannot make a recommendation with sufficient confidence. then a collabora tive recommendation is attempted. This switching hybrid does not completely avoid the ramp-up pro- blem, since both the collaborative and the content-based systems have the ' new user problem. However, Daily Learner's content-based technique is nearest-neighbor which does not require a large number of examples for accurate classification What the collaborative technique provides in a switching hybrid is the ability to cross genres, to come up with recommendations that are not close in a semantic way to the items previous rated highly, but are still relevant. For example, in the case of Daily Learner, a user who is interested in the Microsoft anti-trust trial might also be interested in the aol/Time Warner merger. Content matching would not be likely to recommend the merger stories, but other users with an interest in corporate power in the high-tech industry may be rating both sets of stories highly, enabling the system to make the recommendation colla boratively Daily Learner's hybrid has a 'fallback'character- the short-term model is always used first and the other technique only comes into play when that technique fails Tran and Cohen(1999)proposed a more straightforward switching hybrid. In their and one long-term, and the fallback strategy is short-term/collaborative/long-ter e short-term 3 Actually Billsus' system has two content-based recommendation algorithms, or Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission