ER-SCoR - Recommender System

This code is an implementation of ER-SCoR method an equal ratings impact-based recommender system built upon synthetic coordinates.

This code is a simple (not speed optimized) implementation of ER-SCoR method proposed in [1].

You can find more details in

https://sites.google.com/site/costaspanagiotakis/research/er-scor

Files:

getERSCOR.m: Matlab function that implements the ER-SCoR [1]

The datasets and code can be found in

https://sites.google.com/site/costaspanagiotakis/research/er-scor

Short description:

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.

We will appreciate if you cite our papers in your work:

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

https://doi.org/10.1007/s13042-026-03222-1

[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.

[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.