基于巴氏系数的协同过滤算法
Collaborative Filtering Algorithm Based Bhattacharyya Coefficient
DOI: 10.12677/CSA.2017.75058, PDF, HTML, XML, 下载: 1,448  浏览: 2,119 
作者: 姜少鑫*, 陈 彩, 梁 毅：北京工业大学计算机学院，北京
关键词: 协同过滤算法；修正余弦相似度；巴氏系数；稀疏性问题；Collaborative Filtering Algorithm； Similarity； Bhattacharyya Coefficient； Sparsely
摘要: 为了克服协同过滤算法的稀疏性问题和传统相似度计算方法过度依赖共同评分的问题，本文引入巴氏系数改进修正余弦相似度，进而提出基于巴氏系数的协同过滤算法（CFBC）。改进的算法考虑了项目全局评分信息和局部评分信息，克服了对于共同评分项的依赖。为了证明CFBC算法的有效性，我们基于已有的相似度计算方法实现了协同过滤算法，实验结果表明CFBC算法提高了推荐的准确性。
Abstract: In order to solve the problems of Collaborative Filtering in terms of sparsely and the traditional similarity calculation method which relys on co-rated ratings too much, we utilize Bhattacharyya coefficient to improve the adjusted-cosine method. In this paper, we propose a Collaborative Fil-tering Algorithm based on Bhattacharyya Coefficient (CFBC). The proposed algorithm has considered both the global ratings and the local ratings，and overcomes the dependence of co-rated items. To prove the efficiency of CFBC, this paper has compared the neighborhood based on CFs using state-of-the-art similarity measures with the proposed algorithm based on CF in terms of performance. As a result, the CFBC has improved the accuracy of recommendation.
文章引用：姜少鑫, 陈彩, 梁毅. 基于巴氏系数的协同过滤算法[J]. 计算机科学与应用, 2017, 7(5): 473-480. https://doi.org/10.12677/CSA.2017.75058

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