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Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

机译：电子商务中基于熵的协同过滤算法和个性化推荐研究

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摘要

Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users' hidden interests. Using the data selected from Movie-lens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm.
机译：在介绍基于用户和基于项目的协同过滤算法的基础上，分析了这两种算法的相关问题，提出了一种新的基于熵的推荐算法。针对传统相似度测量方法的弊端，提出了一种改进的相似度测量方法。基于熵的协同过滤算法有助于解决冷启动问题并发现用户的潜在兴趣。使用从“电影镜头”和“书本穿越”数据集中选择的数据以及MAE准确性度量，通过实验比较了三种不同的协作过滤推荐算法。实验方案和结果进行了详细讨论。结果表明，与基于用户的算法相比，基于熵的算法提供了更好的推荐质量，并且可以达到与基于项目的算法相当的推荐精度。最后提出了一种基于Web服务技术的B2B电子商务推荐应用解决方案，该解决方案采用了基于熵的协同过滤推荐算法。

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