基于径向基函数插值与SVM的协同过滤算法

doi:10.3969/j.issn.1006-2475.2015.08.020

摘要/Abstract

摘要： 为了解决在推荐系统中由于数据的稀疏性导致协同过滤算法准确率低下的问题，提出一种混合径向基函数插值和SVM分类的协同过滤推荐算法。该方法先利用径向基函数插值方法对训练数据中缺失数据进行预测与填补，然后利用支持向量机方法根据插值后的数据来对未知数据进行预测，从而形成推荐列表为系统提供推荐服务。实验结果表明该方法克服了数据质量对推荐算法的影响，相比其他SVM方法具有更高的准确率和稳定性。


关键词: 协同过滤, 径向基函数, 插值, 支持向量机

Abstract: Due to the fact that the data sparseness leads to the low accuracy of a collaborative filtering recommendation system, this paper proposed a collaborative filtering algorithm based on Radial Basis Function (RBF) interpolation and SVM. The algorithm first uses the RBF interpolation to fill the missing data in training data set, and then a SVM classifier is introduced to predict the label for testing data by using the interpolated data set as training set. The test result shows that the method overcomes the impact of data quality on the recommended algorithm, and it outperforms other methods in accuracy and stability.


Key words: collaborative filtering, radial basis function, interpolation, support vector machine

中图分类号:

TP301.6

詹增荣,曾青松. 基于径向基函数插值与SVM的协同过滤算法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.08.020.

ZHAN Zeng-rong, ZENG Qing-song. A Collaborative Filtering Algorithm Based on Radial Basis Function Interpolation and SVM[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.08.020.

参考文献

［1］ Symeonidis P, Nanopoulos A, Papadopoulos A N, et al. Collaborative recommender systems: Combining effectiveness and efficiency［J］. Expert Systems with Applications, 2008,34(4):2995-3013.
［2］ Leung Cane Wing-ki, Chan Stephen Chi-fai, Chung Fu-lai. An empirical study of a cross-level association rule mining approach to cold-start recommendations［J］. Knowledge-Based Systems, 2008,21(7):515-529.
［3］〖JP2〗Weng Sung-shun, Lin Binshan, Chen Wen-tien. Using contextual information and multidimensional approach for recommendation［J］. Expert Systems with Applications, 2009,36(2):1268-1279.
［4］ Liu Duen-ren, Shih Ya-yueh. Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences［J］. Journal of Systems and Software, 2005,77(2):181-191.
［5］ Hernández del Olmo Félix, Gaudioso Elena, Martin Eduardo H. The task of guiding in adaptive recommender systems［J］. Expert Systems with Applications, 2009,36(2):1972-1977.
［6］ Kunaver Matev, Porl Toma, Poga〖KG-*4〗c〖DD(-1*2〗〖HT5”〗ˇ〖DD)〗nik Matev, et al. Optimisation of combined collaborative recommender systems［J］. AEU-International Journal of Electronics and Communications, 2007,61(7):433-443.
［7］ Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases［J］. ACM SIGMOD Record, 1993,22(2):207-216.
［8］ Goldberg D, Nichols D A, Oki B M, et al. Using collaborative filtering to weave an information tapestry［J］. Communications of the ACM, 1992,35(12):61-70.
［9］ Huang Cheng-lung, Huang Wei-liang. Handling sequential pattern decay: Developing a two-stage collaborative recommender system［J］. Electronic Commerce Research and Applications, 2009,8(3):117-129.
［10］Huang Zan, Zeng Daniel, Chen Hsinchun. A comparison of collaborative-filtering recommendation algorithms for e-commerce［J］. Intelligent Systems, IEEE, 2007,22(5):68-78.
［11］Breese S J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering［C］// Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 1998:43-52.
［12］Das S A, Datar M, Gstg A, et al. Google news personalization: Scalable online collaborative filtering［C］// Proceedings of the 16th International Conference on World Wide Web. 2007:271-280.
［13］Fan Jiaqi, Pan Weimin, Jiang Lisi. An improved collaborative filtering algorithm combining content-based algorithm and user activity［C］// Proceedings of 2014 International Conference on Big Data and Smart Computing(BIGCOMP). 2014:88-91.
［14］Ji Ke, Shen Hong. Using category and keyword for personalized recommendation: A scalable collaborative filtering algorithm［C］// 2014 6th International Symposium on Parallel Architectures, Algorithms and Programming(PAAP). 2014:197-202.
［15］Xie Feng, Chen Zhen, Shang Jiaxing, et al. Item similarity learning methods for collaborative filtering recommender systems［C］// 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (AINA). 2015:896-903.
［16］Datta S, deep Das J, Gupta P, et al. SCARS: A scalable context-aware recommendation system［C］// 2015 3rd International Conference on Computer, Communication, Control and Information Technology(C3IT). 2015:1-6.
［17］Robles V, Larranaga P, Menasalvas E, et al. Improvement of naive Bayes collaborative filtering using interval estimation［C］// Proceedings of IEEE/WIC International Conference on Web Intelligence. 2003:168-174.
［18］张锋,常会友. 使用BP神经网络缓解协同过滤推荐算法的稀疏性问题［J］. 计算机研究与发展, 2006,43(4):667-672.
［19］Wei Suyun, Ye Ning, Zhang Shuo, et al. Collaborative filtering recommendation algorithm based on item clustering and global similarity［C］// 2012 5th International Conference on Business Intelligence and Financial Engineering(BIFE). 2012:69-72.
［20］Zhang Tong, Iyengar S V. Recommender systems using linear classifiers［J］. The Journal of Machine Learning Research, 2002,2:313-334.
［21］Basu C, Hirsh H, Cohen W. Recommendation as classification: Using social and content-based information in recommendation［C］// Proceedings of the 15th National Conference on Artificial Intelligence. 1998:714-720.
［22］Gr〖KG-*4〗c〖DD(-1*2〗〖HT5”〗ˇ〖DD)〗ar Miha, Fortuna Bla, Mladeni〖KG-*4〗c〖DD(-1*2〗〖HT5”〗ˇ〖DD)〗 Dunja, et al. kNN Versus SVM in the Collaborative Filtering Framework［M］. Springer, 2006:251-260.
［23］Ghazarian S, Nematbakhsh M A. Enhancing memory-based collaborative filtering for group recommender systems［J］. Expert Systems with Applications, 2015,42(7):3801-3812.
［24］Zhang Fuzhi, Zhou Quanqiang. HHT-VM: An online method for detecting profile injection attacks in collaborative recommender systems［J］. Knowledge-Based Systems, 2014,65:96-105.
［25］Lichtenthler C, Schmidt-Thieme L. Multinomial SVM Item Recommender for Repeat-Buying Scenarios［M］. Springer, 2014:189-197.
［26］Vapnik V. The Nature of Statistical Learning Theory［M］. Springer, 1999.
［27］Grouplens Research. Movielens Data Sets.［EB/OL］. http://grouplens.org/datasets/movielens/, 2014-05-01.
［28］Min Sung-hwan, Han Ingoo. Recommender Systems Using Support Vector Machines［M］. Springer, 2005:387-393.
［29］Chang Chih-chung,Lin Chih-jen. LIBSVM: A library for support vector machines［J］. ACM Transactions on Intelligent Systems and Technology, 2011,2(3):1-27.

[1]	杨博, 庄毅. 基于AOA-MSVM的控制集群故障检测方法[J]. 计算机与现代化, 2023, 0(12): 112-116.
[2]	杨孙哲, 孙爱珍. 基于HSV颜色与LBP纹理特征的水稻氮素营养诊断[J]. 计算机与现代化, 2023, 0(07): 86-92.
[3]	申志, 李元 . 基于KPCA和SSA优化SVM的非线性过程故障检测#br#[J]. 计算机与现代化, 2023, 0(06): 15-20.
[4]	尹建丰, 卫鑫, 顾雄伟, 黄凯, 魏敏捷. 基于图像阈值优化及改进SVM的电表数字识别[J]. 计算机与现代化, 2023, 0(05): 106-110.
[5]	刘国丽, 徐洪楠, 谭有倩. 结合专家信任的协同过滤推荐算法研究[J]. 计算机与现代化, 2022, 0(11): 60-68.
[6]	文勇军, 何环晶, 唐立军, . 基于LDA的隐式标签协同过滤推荐算法[J]. 计算机与现代化, 2022, 0(03): 53-58.
[7]	王哲, 王龙达, 刘罡, 王兴成, 王忠君, 鲍鲁杰. 基于三次样条插值的自动入库泊车免疫粒子群改进算法[J]. 计算机与现代化, 2022, 0(02): 7-12.
[8]	赵延平, 王芳, 夏杨. 基于支持向量机的短文本分类方法[J]. 计算机与现代化, 2022, 0(02): 92-96.
[9]	赵聪慧, 冯庆胜. 基于DT-CWT和SVM的踏面旋转不变纹理提取算法[J]. 计算机与现代化, 2021, 0(12): 85-90.
[10]	耿化聪, 梁宏涛, 刘国柱. 基于知识图谱与协同过滤的饮食推荐算法[J]. 计算机与现代化, 2021, 0(08): 24-29.
[11]	黄皓, 陈荔. 基于信息熵和改进相似度协同过滤算法[J]. 计算机与现代化, 2021, 0(06): 29-34.
[12]	陈翔, 邹庆年, 谢绍宇, 陈翠琼. 基于迁移学习策略的压板开关状态识别[J]. 计算机与现代化, 2021, 0(05): 120-126.
[13]	彭程, 张进才, 陈勇, 李斌 . 基于Akima插值的地面沉降等值面服务构建方法[J]. 计算机与现代化, 2021, 0(03): 63-69.
[14]	王文蔚, 肖军弼, 程鹏, 张悦. 基于SDN的DDoS攻击防御系统[J]. 计算机与现代化, 2021, 0(02): 117-121.
[15]	张丽洁, 沙秀艳, 尹传存, 段钧陶, 张欣怡, 李紫桐, 姜福蕾. 基于变权优化背景值改进的GM(1,1)灰色预测模型及其应用 [J]. 计算机与现代化, 2021, 0(01): 1-6.