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Recommender Systems (RS) may behave differently depending on the characteristics of the input data, encouraging the development of Hybrid Filtering (HF). There are few works in the literature that explicitly characterize aspects of the input data and how they can lead to better HF solutions. Such work is limited to the scope of combination of Collaborative Filtering (CF) solutions, using only rating prediction accuracy as an evaluation criterion. However, it is known that RS also need to consider other evaluation criteria, such as novelty and diversity, and that HF involving more than one approach can lead to more effective solutions. In this work, we begin to explore this under-investigated area, by evaluating different HF strategies involving CF and Content-Based (CB) approaches, using a variety of data characteristics as extra input data, as well as different evaluation criteria. We found that the use of data characteristics in HF proved to be useful when considering different evaluation criteria. This occurs in spite of the fact that the experimented methods aim at minimizing only the rating prediction errors, without considering other criteria.

A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics