| SBHCF:基于奇异值分解的混合协同过滤推荐算法 |
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| 引用本文： | 苏凡军,唐启桂.SBHCF:基于奇异值分解的混合协同过滤推荐算法[J].电子科技,2016,29(1):44. |
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| 作者姓名： | 苏凡军 唐启桂 |
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| 作者单位： | (上海理工大学 光电信息与计算机工程学院,上海 200000) |
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| 基金项目： | 上海智能家居大规模物联共性技术工程中心基金资助项目(GCZX14014);沪江基金研究基地专项基金资助项目(C14001);广西可信软件重点实验室开放课题基金资助项目(KX201415) |
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| 摘 要： | 针对传统协同过滤中的最近邻查找不够合理导致推荐的准确率较低的困境。提出一个基于矩阵分解的混合相似度算法。该方法融合了基于模型的奇异值矩阵分解算法和基于近邻的协同过滤算法皮尔逊相关系数,并引入阈值和杰卡德系数对相似度进行修正。在公共有效数据集上的实验表明,所提出算法的平均绝对误差比传统的推荐算法至少降低了7.7%,有效提高了推荐准确率。
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| 关 键 词： | 协同过滤 奇异值矩阵分解 杰卡德系数 皮尔逊系数 |
SBHCF: An SVD-based Hybrid Collaborative Filtering Recommendation Algorithm |
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| SU Fanjun,TANG Qigui.SBHCF: An SVD-based Hybrid Collaborative Filtering Recommendation Algorithm[J].Electronic Science and Technology,2016,29(1):44. |
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| Authors: | SU Fanjun TANG Qigui |
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| Affiliation: | (School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200000,China) |
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| Abstract: | The traditional CF recommendation algorithms are poor in accuracy because of its irrational neighbor-retrieve.In this paper,an SVD-based hybrid collaborative filtering algorithm is proposed to solve the challenge.The method combines SVD model-based CF algorithm and PCC memory-based CF algorithm.Several parameters and JACCARD are introduced to revise the similarity.The experiment in the public data set proves that the SBHCF algorithm effectively improves the recommendation accuracy with a reduced MAE by at least 7.7% than the traditional CF algorithm. |
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| Keywords: | collaborative filtering singular value matrix factorization Jaccard coefficient Pearson coefficient |
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