A User Training Error based Correction Approach combined with the Synthetic Coordinate Recommender System

Abstract

We propose a Synthetic Coordinate Recommendation system using a user Training Error based Correction approach (SCoR-UTEC). Synthetic Euclidean coordinates are assigned by SCoR system to users and items, so that, when the system converges, the distance between a user and an item provides an accurate prediction of the user's preference for that item. In this paper, after the SCoR execution, we introduce a stage called UTEC to correct the SCoR recommendations taking into account the error on the training set between users and items and their proximity in the synthetic Euclidean space of SCoR. UTEC is also applicable on any model-based recommender system with positive training error like SCoR. The experimental results demonstrate the efficiency and high performance of the proposed second stage on real world datasets.