ER-SCoR: an equal ratings impact-based recommender system using synthetic coordinates

<title>Abstract</title> In this article, we introduce ER-SCoR, an Equal Ratings impact-based Recommender System built upon Synthetic Coordinates, which is shown to outperform the state-of-the-art algorithmic techniques as well as the original Synthetic Coordinate based Recommendation system (SCoR). <italic>SCoR</italic> assigns a set of synthetic coordinates to every node (both users and items), such as the distance between a user and an item corresponds to an accurate prediction of the user’s preference for that item. ER-SCoR 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. ER-SCoR preserves the advantages of SCoR like parameter-free configuration, robustness to cold-start problems, and linear computational complexity, while achieving faster convergence and improved predictive performance.Extensive experiments across five real-world datasets demonstrate that <italic>ER-SCoR</italic> consistently yields lower <italic>RMSE</italic> compared to existing approaches, and provides meaningful dataset annotations, including identification of outliers, users with similar preferences and items that receive similar user ratings. MSC Classification: 68T20 , 68W50