Collaborative filtering systems make recommendations based on the accumulation of ratings by many users. The process has a case-based reasoning flavor: recommendations are generated by looking at the behavior of other users who are considered similar. However, the features associated with a user are semantically weak compared with those used by CBR systems. This research examines multi-dimensional or semantic ratings in which a system gets information about the reason behind a preference. Experiments show that metrics in which the semantic meaning of each rating is taken into account have markedly superior performance than simpler techniques.
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