Estimating the Value of Multi-Dimensional Data Sets in Context-based Recommender Systems

We propose a method for estimating the expected economic value of multi-dimensional data sets in recommender systems and illustrate the proposed approach using a unique data set combining implicit and explicit ratings with rich content as well as spatio-temporal contextual dimensions and social network data.