基于评分相似性的群稀疏矩阵分解推荐算法

doi:10.11772/j.issn.1001-9081.2017.05.1397

计算机应用 ›› 2017, Vol. 37 ›› Issue (5): 1397-1401.DOI: 10.11772/j.issn.1001-9081.2017.05.1397

基于评分相似性的群稀疏矩阵分解推荐算法

盛伟, 王保云, 何苗, 余英

云南师范大学信息学院, 昆明 650500

收稿日期:2016-10-13 修回日期:2016-12-21 出版日期:2017-05-10 发布日期:2017-05-16
通讯作者: 王保云
作者简介:盛伟(1988-),男,江苏丰县人,硕士研究生,主要研究方向:推荐系统;王保云(1977-),男,云南玉溪人,讲师,博士,主要研究方向:机器学习;何苗(1990-),女,云南曲靖人,硕士研究生,主要研究方向:机器学习;余英(1965-),女,云南昆明人,副教授,硕士,主要研究方向:网络通信。
基金资助:
云南省教育厅科学研究基金资助项目（2014Y145）；云南省哲学社会科学规划项目（QN2015067）；云南师范大学博士启动基金资助项目（01000205020503064）。

Score similarity based matrix factorization recommendation algorithm with group sparsity

SHENG Wei, WANG Baoyun, HE Miao, YU Ying

School of Information Science and Technology, Yunnan Normal University, Kunming Yunnan 650500, China

Received:2016-10-13 Revised:2016-12-21 Online:2017-05-10 Published:2017-05-16
Supported by:
This work is partially supported by the Scientific Research Foundation of Education Department of Yunnan Province (2014Y145), the Philosophical and Social Science Program of Yunnan Province (QN2015067), the Doctoral Research Starting Foundation of Yunnan Normal University (01000205020503064).

摘要/Abstract

摘要： 如何提高系统的推荐精度，是当前推荐系统面临的重要问题。对矩阵分解模型进行了研究，针对评分数据的群结构性问题，提出了一种基于评分相似性的群稀疏矩阵分解模型（SSMF-GS）。首先，根据用户的评分行为对评分数据矩阵进行分群，获得相似用户群评分矩阵；然后，通过SSMF-GS算法对相似用户群评分矩阵进行群稀疏矩阵分解；最后，采用交替优化算法对模型进行求解。所提模型可以筛选出不同用户群的偏好潜在项目特征，提升了潜在特征的可解释性。在GroupLens网站上提供的MovieLens数据集上进行仿真实验，实验结果表明，所提算法可以显著提高预测精度，平均绝对误差（MAE）及均方根误差（RMSE）指标均表现出良好的性能。

关键词: 群稀疏, 矩阵分解, L_2, 1范数正则化, 潜在特征

Abstract: How to improve the accuracy of recommendation is an important issue for the current recommendation system. The matrix decomposition model was studied, and in order to exploit the group structure of the rating data, a Score Similarity based Matrix Factorization recommendation algorithm with Group Sparsity (SSMF-GS) was proposed. Firstly, the scoring matrix was divided into groups according to the users' rating behavior, and the similar user group scoring matrix was obtained. Then, similar users' rating matrix was decomposed in group sparsity by SSMF-GS algorithm. Finally, the alternating optimization algorithm was applied to optimize the proposed model. The latent item features of different user groups could be filtered out and the explanability of latent features was enhanced by the proposed model. Simulation experiments were tested on MovieLens datasets provided by GroupLens website. The experimental results show that the proposed algorithm can improve recommendation accuracy significantly, and the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) both have good performance.

Key words: group sparsity, matrix factorization, L_2,1-norm regularization, latent feature

中图分类号:

TP181

盛伟, 王保云, 何苗, 余英. 基于评分相似性的群稀疏矩阵分解推荐算法[J]. 计算机应用, 2017, 37(5): 1397-1401.

SHENG Wei, WANG Baoyun, HE Miao, YU Ying. Score similarity based matrix factorization recommendation algorithm with group sparsity[J]. Journal of Computer Applications, 2017, 37(5): 1397-1401.

参考文献

[1] 冷亚军, 陆青, 梁昌勇. 协同过滤推荐技术综述[J].模式识别与人工智能, 2014, 27(8):720-734.(LENG Y J, LU Q, LIANG C Y. Survey of recommendation based on collaborative filtering[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(8):720-734.)
[2] PAGARE R, PATIL S A. Study of collaborative filtering recommendation algorithm-scalability issue[J]. International Journal of Computer Applications, 2013, 67(25):10-15.
[3] KOREN Y, BELL R, VOLINSKY C. Matrix factorization tech-niques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[4] SALAKHUTDINOV B R, MNIH A. Probabilistic matrix factorization[C]//Proceedings of the 21st Annual Conference on Neural Information Processing Systems. New York:Curran Associate Inc,2008:1257-1264.
[5] ZHANG Y, KOREN J. Efficient Bayesian hierarchical user modeling for recommendation system[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2007:47-54.
[6] PHAM T A N, LI X, CONG G, et al. A general graph-based model for recommendation in event-based social networks[C]//Proceedings of the 2015 IEEE 31st International Conference on Data Engineering. Piscataway, NJ:IEEE, 2015:567-578.
[7] BOKDE D, GIRASE S, MUKHOPADHYAY D. Matrix factorization model in collaborative filtering algorithms:a survey[J]. Procedia Computer Science, 2015, 49(1):136-146.
[8] YUAN T, CHENG J, ZHANG X, et al. Recommendation by mining multiple user behaviors with group sparsity[EB/OL].[2016-10-11]. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8267/8424.
[9] HUANG J, ZHANG T. The benefit of group sparsity[J]. The Annals of Statistics, 2010, 38(4):1978-2004.
[10] 郑秋中, 徐军. 一种基于群稀疏特征选择的图像检索方法[J]. 计算机应用研究, 2014, 31(9):2867-2872.(ZHENG Q Z, XU J. Group sparse based feature selection for image retrieval[J]. Application Research of Computers, 2014, 31(9):2867-2872.)
[11] 陈蕾, 杨庚, 陈正宇,等. 基于结构化噪声矩阵补全的Web服务QoS预测[J]. 通信学报, 2015, 36(6):49-59. (CHEN L, YANG G, CHEN Z Y, et al. Web services QoS prediction via matrix completion with structural noise[J]. Journal on Communications, 2015, 36(6):49-59.)
[12] FREY B J, DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814):972-976.
[13] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]//Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA:Morgan Kaufmann Publishers, 1998:43-52.
[14] LIN Z, CHEN M, MA Y. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL].[2016-10-11].https://arxiv.org/pdf/1009.5055v3.pdf.

[1]	王锦凯, 贾旭. 基于迁移孪生非负矩阵分解的静脉识别算法[J]. 计算机应用, 2021, 41(3): 898-903.
[2]	温雯, 刘芳, 蔡瑞初, 郝志峰. 面向群组用户时序行为的动态推荐算法[J]. 计算机应用, 2021, 41(1): 60-66.
[3]	田保军, 刘爽, 房建东. 融合主题信息和卷积神经网络的混合推荐算法[J]. 计算机应用, 2020, 40(7): 1901-1907.
[4]	王锦凯, 贾旭. 基于加权正交约束非负矩阵分解的车脸识别算法[J]. 计算机应用, 2020, 40(4): 1050-1055.
[5]	成其伟, 陈启买, 贺超波, 刘海. 基于改进对称二值非负矩阵分解的重叠社区发现方法[J]. 计算机应用, 2020, 40(11): 3203-3210.
[6]	沈学利, 李子健, 赫辰皓. 基于评分填充与信任信息的混合推荐算法[J]. 计算机应用, 2020, 40(10): 2789-2794.
[7]	刘颖, 梁楠楠, 李大湘, 杨凡超. 基于光谱距离聚类的高光谱图像解混算法[J]. 计算机应用, 2019, 39(9): 2541-2546.
[8]	杨燕琳, 冶忠林, 赵海兴, 孟磊. 基于高阶近似的链路预测算法[J]. 计算机应用, 2019, 39(8): 2366-2373.
[9]	陈善学, 储成泉. 基于稀疏和正交约束非负矩阵分解的高光谱解混[J]. 计算机应用, 2019, 39(8): 2276-2280.
[10]	杨亮东, 杨志霞. 稀疏限制的增量式鲁棒非负矩阵分解及其应用[J]. 计算机应用, 2019, 39(5): 1275-1281.
[11]	邵长城, 陈平华. 融合社交网络和图像内容的兴趣点推荐[J]. 计算机应用, 2019, 39(5): 1261-1268.
[12]	王磊, 任航, 龚凯. 基于多维信任和联合矩阵分解的社会化推荐方法[J]. 计算机应用, 2019, 39(5): 1269-1274.
[13]	李飞, 杜亮, 任超宏. 基于全局融合的多核概念分解算法[J]. 计算机应用, 2019, 39(4): 1021-1026.
[14]	李艳生, 刘园, 张毅. 基于感知掩蔽的重构非负矩阵分解单通道语音增强算法[J]. 计算机应用, 2019, 39(3): 894-898.
[15]	余江兰, 李向利, 赵朋飞. 基于核技巧和超图正则的稀疏非负矩阵分解[J]. 计算机应用, 2019, 39(3): 742-749.

基于评分相似性的群稀疏矩阵分解推荐算法

Score similarity based matrix factorization recommendation algorithm with group sparsity

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics