We propose ER-SCoR [1], an Equal Ratings impact-based Recommender System built upon Synthetic Coordinates (see Fig. 1.  )

• SCoR [2-3] assigns a set of synthetic coordinates to every node (both users and items), such that the distance between a user and an item corresponds to an accurate prediction of the user’s preference for that item. 

• ER-SCoR [1] enhances this model by 

  • (i) enforcing equal contributions from all ratings during coordinate updates, and 

  • (ii) incorporating three additional terms into the recommendation process: a global system belief, a user-specific belief, and an item-specific belief. These modifications constitute fundamental changes in the core system architecture and improve convergence speed, accuracy, and stability.

Experiments - Downloads

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

  • You can download the datasets of the method used in [1]  from (.zip).

Related Publications

[1] C. Panagiotakis, H. Papadakis, and P. Fragopoulou, ER-SCoR: An Equal Ratings Impact-Based Recommender System Using Synthetic Coordinates,   submitted to International Journal of Machine Learning and Cybernetics, 2026. 

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

[3]  C. Panagiotakis, H. Papadakis, A. Papagrigoriou and P. Fragopoulou, Improving Recommender Systems via a Dual Training Error based Correction Approach,   Expert Systems with Applications, 2021.