CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis
CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis
复制标题
CBMR：基于项目的协同过滤推荐算法的优化 MapReduce 与实证分析
DOI：
10.1002/cpe.4092
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发表时间：
2017-05-25
影响因子：
2
通讯作者：
He, Kejing
中科院分区：
文献类型：
--
作者：
Li, Chenyang;He, Kejing
基于项目的协同过滤（CF）是一种基于模型的推荐算法。在该算法中，通过使用多个相似度量来计算项目之间的相似度，然后使用这些相似值来预测用户的评分。但是，如果项目和用户的数量增长到数百万，则基于项目的CF的可伸缩性和处理效率可能会受到一些硬件限制的阻碍。为了解决这一问题，我们提出了一种优化的MapReduce基于项目的CF算法，并结合实证分析。通过对真实数据集的大量实验，我们通过评估其执行时间并将其shuffle阶段开销与传统方法进行比较，证明了我们的方法的优势。实验结果表明，该方法在处理大规模数据集时具有更好的性能。
Item-based collaborative filtering (CF) is amodel-based algorithm for making recommendations. In the algorithm, the similarity between items are calculated by using a number of similarity measures, and then these similarity values are used to predict ratings for users. However, if the number of items and users grows to millions, the scalability and the processing efficiency of item-based CF can be hindered by some hardware constraints. To solve this problem, we propose an optimized MapReduce for item-based CF algorithm integrated with empirical analysis. Through extensive experiments on real-world datasets, we demonstrate the advantages of our approach by evaluating its execution time and by comparing its shuffle phase overhead with the conventional methods. The experimental results suggest that our approach has better performance when processing large-scale datasets.