Three‐way recommendation integrating global and local information

Abstract

The matrix factorisation approach computes a low-rank approximation of the incomplete user-item rating matrix. Existing approaches suffer from under-fitting due to the use of global information for all users and items. In this study, the authors propose a three-way recommendation model that integrates global and local information. This new model has a number of main aspects. The first is rating prediction with global and local information. A clustering and two matrix factorisation algorithms are employed for this purpose. The second is the computation of recommendation thresholds based on the decision-theoretic rough set model. Misclassification and promotion costs are considered simultaneously to build the cost matrix. The last is the determination of the recommender actions based on the prediction and thresholds. Experimental results on the well-known datasets show that authors’ proposed model improves recommendation quality in terms of average cost.