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A Machine Learning Based Implementation of Product and Service Recommendation Models

机译:基于机器学习的产品和服务推荐模型的实现

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Most of the internet based companies are now relying on the capabilities of recommendation models to increase their product sales. By applying efficient recommendation models businesses can track their customer preferences and effectively recommend products to users thereby increasing their sales. This paper describes the prototype implementation of two recommendations models using machine learning algorithms. The first prototype system is a banking service recommendation system and the second one is a movie recommendation system. These prototype implementations are evidence of how effectively machine learning algorithms can be applied for designing recommendation models.
机译:大多数基于互联网的公司现在依靠推荐模型的能力来提高其产品销售。通过应用有效的推荐模型,企业可以跟踪客户偏好,并有效地推荐给用户的产品,从而提高其销售。本文介绍了使用机器学习算法的两种建议模型的原型实现。第一个原型系统是银行业务推荐系统,第二个是电影推荐系统。这些原型实现是如何有效地应用于设计推荐模型的机器学习算法的证据。

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