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A weight-based item recommendation approach for electronic commerce systems

机译：电子商务系统中基于权重的项目推荐方法

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摘要

The recommendation system is a useful tool that can be employed to identify potential relationships between items and users in electronic commerce systems. Consequently, it can remarkably improve the efficiency of a business. The topic of how to enhance the accuracy of a recommendation has attracted much attention by researchers over the past decade. As such, many methods to accomplish this task have been introduced. However, more complex calculations are normally necessary to achieve a higher accuracy, which is not suitable for a real-time system. Hence, in this paper, we propose a weight-based item recommendation approach to provide a balanced formula between the recommended accuracy and the computational complexity. The proposed methods employ a newly defined distance to describe the relationship between the users and the items, after which the recommendations and predictive algorithms are developed. A data analysis based on the MovieLens datasets indicates that the methods applied can obtain suitable prediction accuracy and maintain a relatively low computational complexity.
机译：推荐系统是一种有用的工具，可用于识别电子商务系统中项目与用户之间的潜在关系。因此，它可以显着提高业务效率。在过去的十年中，如何提高建议的准确性这一主题已引起研究人员的广泛关注。这样，已经介绍了完成该任务的许多方法。但是，通常需要更复杂的计算才能获得更高的精度，这不适用于实时系统。因此，在本文中，我们提出了一种基于权重的项目推荐方法，以在推荐的准确性和计算复杂性之间提供平衡的公式。所提出的方法采用新定义的距离来描述用户与项目之间的关系，然后提出建议和预测算法。基于MovieLens数据集的数据分析表明，所应用的方法可以获得适当的预测精度，并保持较低的计算复杂度。

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