Abstract
In this paper we present a hybrid filtering algorithm that attempts to deal with low prediction Coverage, a problem especially present in sparse datasets. We focus on Item HyCoV, an implementation of the proposed approach that incorporates an additional User-based step to the base Item-based algorithm, in order to take into account the possible contribution of users similar to the active user. A series of experiments were executed, aiming to evaluate the proposed approach in terms of Coverage and Accuracy. The results show that Item HyCov significantly improves both performance measures, requiring no additional data and minimal modification of existing filtering systems.
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