In recent years, a variety of techniques have been proposed for calculating personal recommendations, including collaborative filtering, content-based methods and knowledge-based techniques. To improve performance, these algorithms can be combined in hybrid recommenders. The Sugestio team is also doing research on various hybrid recommendation techniques. Here, we provide a brief overview of the state of the art.

A merging solution is a simple technique that combines the recommendations of multiple recommenders. Top-N recommendation lists of the various algorithms are joined to create a new list consisting of a mix of these suggestions. A more intelligent way to combine the recommendations of different algorithms is by using a weighting scheme. These weighting solutions combine the scores of several recommender systems into a final recommendation score based on weights expressing the confidence in the various algorithms. These algorithm weights may be altered during operation to fine-tune the hybrid recommender.
Instead of combining the recommendations, switching solutions select the best recommendation algorithm depending on the available data. This hybrid recommender might select a content-based algorithm for sparse profile data and switch to a collaborative filter when more data becomes available. Cascade solutions consist of multiple algorithms refining each other. This technique could be applied to restaurant recommendations: a collaborative filtering algorithm determines the best restaurants according to the personal profile of the end-user. Next, a knowledge-based filter eliminates irrelevant restaurants based on the kitchen type, geographical location, price range, etc.

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