SCoR: A Synthetic Coordinate based Recommender system. Papadakis, H., Panagiotakis, C., & Fragopoulou, P. Expert Syst. Appl., 79:8–19, August, 2017. ![SCoR: A Synthetic Coordinate based Recommender system [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer preferences. In this paper, we propose SCoR, a Synthetic Coordinate based Recommendation system which is shown to outperform the most popular algorithmic techniques in the field, approaches like matrix factorization and collaborative filtering. SCoR assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms. Furthermore, it provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers. SCoR is compared against nine other state-of-the-art recommender systems, sever of them based on the well known matrix factorization and two on collaborative filtering. The comparison is performed against four real datasets, including a brief version of the dataset used in the well known Netflix challenge. The extensive experiments prove that SCoR outperforms previous techniques while demonstrating its improved stability and high performance.
@article{papadakis_scor_2017,
title = {{SCoR}: {A} {Synthetic} {Coordinate} based {Recommender} system},
volume = {79},
issn = {0957-4174},
url = {http://www.sciencedirect.com/science/article/pii/S0957417417301070},
doi = {10.1016/j.eswa.2017.02.025},
abstract = {Recommender systems try to predict the preferences of users for specific
items, based on an analysis of previous consumer preferences. In this
paper, we propose SCoR, a Synthetic Coordinate based Recommendation system
which is shown to outperform the most popular algorithmic techniques in
the field, approaches like matrix factorization and collaborative
filtering. SCoR assigns synthetic coordinates to nodes (users and items),
so that the distance between a user and an item provides an accurate
prediction of the user’s preference for that item. The proposed framework
has several benefits. It is parameter free, thus requiring no fine tuning
to achieve high performance, and is more resistance to the cold-start
problem compared to other algorithms. Furthermore, it provides important
annotations of the dataset, such as the physical detection of users and
items with common and unique characteristics as well as the identification
of outliers. SCoR is compared against nine other state-of-the-art
recommender systems, sever of them based on the well known matrix
factorization and two on collaborative filtering. The comparison is
performed against four real datasets, including a brief version of the
dataset used in the well known Netflix challenge. The extensive
experiments prove that SCoR outperforms previous techniques while
demonstrating its improved stability and high performance.},
journal = {Expert Syst. Appl.},
author = {Papadakis, Harris and Panagiotakis, Costas and Fragopoulou, Paraskevi},
month = aug,
year = {2017},
keywords = {Graph, Matrix factorization, Netflix, Recommender systems, Synthetic coordinates, Vivaldi},
pages = {8--19},
}
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SCoR assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms. Furthermore, it provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers. SCoR is compared against nine other state-of-the-art recommender systems, sever of them based on the well known matrix factorization and two on collaborative filtering. The comparison is performed against four real datasets, including a brief version of the dataset used in the well known Netflix challenge. 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