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

Abstract 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). SCoR 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 ER-SCoR consistently yields lower RMSE 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