SCoR: A Synthetic Coordinate based Recommender system
SCoR: A Synthetic Coordinate based Recommender system
复制标题
DOI：
10.1016/j.eswa.2017.02.025
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
H. Papadakis;C. Panagiotakis;P. Fragopoulou
中科院分区：
文献类型：
--
作者：
H. Papadakis;C. Panagiotakis;P. Fragopoulou
推荐系统试图预测用户对特定物品的偏好，基于对先前消费者偏好的分析。在本文中，我们提出了SCoR，一个综合坐标为基础的推荐系统，这是优于最流行的算法技术在该领域，如矩阵分解和协同过滤的方法。SCoR将合成坐标分配给节点（用户和项目），以便用户和项目之间的距离可以准确预测用户对该项目的偏好。拟议的框架有几个好处。它是无参数的，因此不需要微调来实现高性能，并且与其他算法相比更能抵抗冷启动问题。此外，它还提供了数据集的重要注释，例如用户和具有共同和独特特征的项目的物理检测以及异常值的识别。SCoR与其他九个最先进的推荐系统进行了比较，其中几个基于众所周知的矩阵分解，两个基于协同过滤。该比较是针对四个真实的数据集进行的，包括在众所周知的Netflix挑战中使用的数据集的简短版本。大量的实验证明，SCoR优于以前的技术，同时证明其改进的稳定性和高性能。
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.