Comparing accuracy of cosine-based similarity and correlation-based similarity algorithms in tourism recommender systems

Elnaz Bigdeli, Z. Bahmani
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引用次数: 21

Abstract

Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. The predictive accuracy of various methods in tourism recommender domains is compared. On the other hand, we have designed and implemented a recommender system in e-tourism in order to compare performance of these algorithms. Finally, we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm.
旅游推荐系统中基于余弦相似度算法与基于关联相似度算法的准确率比较
推荐系统作为人工智能领域的成功应用,有着悠久的历史。不同的电子商务平台提供的产品数量的增长,导致了一种可以帮助客户选择和购买产品的技术。协同过滤或推荐系统使用关于用户偏好的数据库来预测新用户可能喜欢的其他主题或产品。本文介绍了为此设计的一些算法,包括基于余弦的相似度算法和基于相关的相似度算法。比较了各种方法在旅游推荐领域的预测精度。另一方面,为了比较这些算法的性能,我们设计并实现了一个电子旅游推荐系统。最后得出基于关联的相似度算法优于基于余弦的相似度算法的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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