ResBeMF: Improving Prediction Coverage of Classification based Collaborative Filtering
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Reliability measures associated to machine learning model predictions are critical to reinforcing user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also reliability enjoy greater popularity. In the field of recommender systems, reliability is crucial, since users tend to prefer those recommendations that are sure to interest them, i.e. high predictions with high reliabilities. In this paper we present ResBeMF, a new recommender system based on collaborative filtering that provides reliabilities associated with its predictions. Experimental results show that ResBeMF offers greater customization than other models, allowing to adjust the balance between prediction quality and prediction reliability.
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