计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 106-110.doi: 10.11896/j.issn.1002-137X.2019.08.017
康林瑶, 唐兵, 夏艳敏, 张黎
KANG Lin-yao, TANG Bing, XIA Yan-min, ZHANG Li
摘要: 协同过滤(CF)已经在推荐系统中得到了广泛的应用。但是随着用户和项目规模的增大,协同过滤算法的运行效率以及结果的正确性会大大降低。针对这一问题,文中提出了一种基于GPU的非负矩阵分解(NMF)的并行协同过滤方法,充分利用NMF数据降维和特征提取的优势以及CUDA的多核并行计算模式,进行维数简化和用户的相似性计算。该算法在提高精确性的同时降低了计算耗费,可以较好地解决协同过滤推荐系统所存在的稀疏性和扩展性等问题,快速产生精确的个性化推荐结果。基于NVIDIA CUDA设备和真实的MovieLens用户评分数据集,将所设计的并行NMF协同过滤算法与传统的基于用户的和基于物品的协同过滤算法进行了比较,实验结果表明,所设计的并行NMF协同过滤算法达到了较快的处理速度以及较高的推荐准确率。
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