We propose a Synthetic Coordinate Recommendation system using a User Training Error based Correction approach (SCoR-UTEC) see Fig. 1.

• Initially, SCoR assigns synthetic Euclidian coordinates to users and items (see Fig . 2)
• In this work, we introduce a second stage (UTEC) after the SCoR execution, that corrects the recommendations of SCoR taking into account the error on the training set between users and items and their proximity in the synthetic Euclidean space of SCoR.
• These corrections can be performed in user view point
• UTEC computes a model that makes zero the recommendation error on the training set, and then applies it on the test set to improve SCoR’s predictions.
• The proposed UTEC approach is applicable on any model-based recommender system with positive training error like SCoR, potentially increasing the accuracy of the recommendations.

Experiments - Downloads

• You can download the matlab code of the SCoR-UTEC method proposed in [1]. The code will be available after the acceptance of paper [1].
• You can download the datasets of the method proposed in [1] from (.zip).

Related Publications

[1] C. Panagiotakis, H. Papadakis, and P. Fragopoulou, A User Training Error based Correction Approach combined with the Synthetic Coordinate Recommender System, UMAP, 2020 (under review).

[2] H. Papadakis, C. Panagiotakis and P. Fragopoulou, SCoR: A Synthetic Coordinate based Recommender System, Expert Systems with Applications, vol. 79, pp.8-19, 2017.