计算机工程与应用2020，Vol.56Issue(1)：83-91,9.DOI:10.3778/j.issn.1002-8331.1810-0142
Slope One算法的改进及其在大数据平台的实现
Improvement of Slope One Algorithm and Its Implementation on Big Data Platform
摘要
Abstract
Aiming at the problem that the original Slope One algorithm ignores the similarities between the projects when calculating the value of the recommendation prediction, and the recommendation inefficiency is low in the big data age, the clustering weighted Slope One recommendation algorithm based on the Spark platform is proposed. Firstly, the nearest neighbor set is generated by Canopy-K-medoids clustering algorithm. Then, in the nearest neighbor set, the Slope One algorithm is used to estimate the similarity between the weighted items. Finally, parallelization is implemented on the Spark platform. The experiments in film data set show that optimization algorithm based on Spark platform compared with the traditional Slope One algorithm and weighted similarity of Slope project One algorithm, improves the precision of recommendation.关键词
Slope One算法/聚类/Spark平台/推荐算法Key words
Slope One algorithm/clustering/Spark platform/recommendation algorithm分类
信息技术与安全科学引用本文复制引用
刘佳耀,王佳斌..Slope One算法的改进及其在大数据平台的实现[J].计算机工程与应用,2020,56(1):83-91,9.基金项目
国家自然科学青年科学基金(No.61505059) （No.61505059）
华侨大学研究生科研创新能力培养计划(No.1611422006) （No.1611422006）
厦门市科技局产学研协同创新项目(No.3502Z20173046). （No.3502Z20173046）
