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An Electronic Commerce Recommendation Algorithm Joining Case-Based Reasoning and Collaborative Filtering

机译：一种电子商务推荐算法加入基于案例的推理和协作滤波

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With the rapid development of network, information technology has provided an unprecedented amount of information resources. It has also led to the problem of information overload. Electronic commerce personalized recommender systems represent services that aim at predicting a customer's interest on information products available in the application domain, using customers' ratings on products. Peoples' experiences often do not enough to deal with the vast amount of available information. Thus, methods to help find products of electronic commerce have attracted much attention from both researchers and vendors. Collaborative filtering technology has proved to be one of the most effective for its simplicity in both theory and implementation. The paper gives an electronic commerce recommendation algorithm combining case-based reasoning and collaborative filtering. Firstly, it uses case-based reasoning to fill the vacant ratings. Then, it produces prediction collaborative filtering. The presented algorithm combining case-based reasoning and collaborative filtering can alleviate the sparsity issue.
机译：随着网络的快速发展，信息技术提供了前所未有的信息资源。它还导致了信息过载的问题。电子商务个性化推荐制度代表了旨在预测客户对应用程序域中的信息产品的兴趣，使用客户的产品评级。人民的经历往往不足以处理大量可用信息。因此，帮助找到电子商务产品的方法引起了研究人员和供应商的许多关注。合作过滤技术已被证明是理论和实施中的简单性最有效的。本文给出了一种电子商务推荐算法，组合了基于案例的推理和协作滤波。首先，它使用基于案例的推理来填补空缺评级。然后，它产生预测协同滤波。所提出的算法组合基于案例的推理和协作滤波可以缓解稀疏问题。

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