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<p>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). <i>SCoR</i> 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 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 <i>ER-SCoR</i> consistently yields lower <i>RMSE</i> compared to existing approaches, and provides meaningful dataset annotations, including identification of outliers, users with similar preferences and items that receive similar user ratings.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Costas Panagiotakis,
  • Harris Papadakis,
  • Paraskevi Fragopoulou

摘要

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 that 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.