An improved content based collaborative filtering algorithm for movie recommendations

A. Pal, Prateek Parhi, M. Aggarwal
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引用次数: 37

Abstract

Recommender system comprises of two prime methods which help in providing meaningful recommendations namely, Collaborative Filtering algorithm and Content-Based Filtering. In this paper, we have used a hybrid methodology which takes advantage of both Content and Collaborative filtering algorithm into account. The algorithm discussed in this article is different from the previous work in this field as it includes a novel method to find the similar content between two items. The paper incorporates an analysis that justifies this new methodology and how it can provide practical recommendations. The above approach is tested on existing user and objects data and produced improved results when compared with other two favourite methods, Pure Collaborative Filtering, and Singular Value Decomposition.
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改进的基于内容的电影推荐协同过滤算法
推荐系统包括协同过滤算法和基于内容的过滤两种主要的方法来提供有意义的推荐。在本文中,我们使用了一种混合方法,该方法同时考虑了内容过滤算法和协同过滤算法的优势。本文所讨论的算法不同于以往在该领域的工作,因为它包含了一种新的方法来寻找两个条目之间的相似内容。本文结合了一项分析,证明了这种新方法的合理性,以及它如何能够提供实用的建议。上述方法在现有用户和对象数据上进行了测试,并与其他两种最受欢迎的方法纯协同过滤和奇异值分解相比,产生了更好的结果。
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