Improving recommender systems via a Dual Training Error based Correction approach

We propose a method to improve the prediction performance of recommender systems via a Dual (user and item) Training Error based Correction approach (DTEC). The proposed method is applied to the Synthetic Coordinate Recommendation system (SCoR) (Papadakis et al., 2017) and to other Ithree state-of-the-art systems. Initially, a recommender system is used Ito provide recommendations for users and items. Subsequently, we introduce a second stage, after initial execution of the recommender system, that improves its predictions taking into account the error in the training set between users and items and their similarity. These corrections can be performed from both user and item viewpoints, and finally a dual system is proposed that efficiently combines both corrections. DTEC computes a model that makes zero the recommendation error in the training set, and then applies it on the test set to improve the rating predictions. The proposed DTEC approach is applicable Ito any model-based recommender system with positive training error, potentially increasing the accuracy of the recommendations. The experimental results demonstrate the efficiency and high performance of DTEC on four well-known, real-world datasets.