基于用户兴趣和社交信任的聚类推荐算法

doi:10.11772/j.issn.1001-9081.2016.05.1273

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1273-1278.DOI: 10.11772/j.issn.1001-9081.2016.05.1273

基于用户兴趣和社交信任的聚类推荐算法

肖晓丽^1,2, 钱娅丽^1,2, 李旦江^1,2, 谭柳斌³

1. 综合交通运输大数据智能处理湖南省重点实验室(长沙理工大学), 长沙 410114;
2. 长沙理工大学计算机与通信工程学院, 长沙 410114;
3. 湖南大众传媒技术学院新媒体技术学院, 长沙 410007

收稿日期:2015-09-29 修回日期:2015-11-04 出版日期:2016-05-10 发布日期:2016-05-09
通讯作者: 钱娅丽
作者简介:肖晓丽(1965-),女,湖南邵阳人,教授,硕士,主要研究方向:数据挖掘、网络安全、移动通信、数据库;钱娅丽(1990-),女,湖南株洲人,硕士研究生,主要研究方向:数据挖掘、网络安全;李旦江(1992-),男,湖南浏阳人,硕士研究生,主要研究方向:数据挖掘、移动通信;谭柳斌(1975-),男,湖南长沙人,主要研究方向:网络安全、移动通信。
基金资助:
国家自然科学基金资助项目(61303043);湖南省自然科学基金资助项目(13JJ4052)。

Clustering recommendation algorithm based on user interest and social trust

XIAO Xiaoli^1,2, QIAN Yali^1,2, LI Danjiang^1,2, TAN Liubin³

1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation(Changsha University of Science and Technology), Changsha Hunan 410114 China;
2. College of Computer and Communication Engineering, Changsha University of Sciences and Technology, Changsha Hunan 410114, China;
3. College of New Media Technology, Hunan Mass Media Vocational Technical College, Changsha Hunan 410007, China

Received:2015-09-29 Revised:2015-11-04 Online:2016-05-10 Published:2016-05-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61303043), the Natural Science Foundation of Hunan Province (13JJ4052).

摘要/Abstract

摘要： 个性化推荐系统中使用最广泛的算法是协同过滤算法,针对该算法存在的数据稀疏和扩展性差问题,提出了一种基于用户兴趣和社交信任的聚类推荐算法。该算法首先基于聚类技术根据用户评分信息将具有相同兴趣的用户聚为一类,并建立基于用户兴趣相近的邻居集合。为了提高兴趣相似度计算的准确性,采用了修正余弦计算公式来消除评分标准的差异问题。然后,引入信任机制,通过定义直接信任、间接信任、传递路径和计算方法来度量社交网络用户之间隐含的信任值,将社交网络转换为信任网络,依据信任程度来创建基于社交信任的邻居集合。通过加权的方式将基于两种邻居集合的预测值融合起来为用户产生项目的推荐。在Douban数据集上进行仿真实验,确定了最优的协调因子值和分类数值,并与基于用户的协同过滤算法和基于信任的推荐算法进行对比,实验结果表明,所提算法的平均绝对误差(MAE)减少了6.7%,准确率(precision)、覆盖(recall)和F1值分别增加了25%、40%和37%,有效提高了推荐系统的推荐质量。

关键词: 协同过滤, 数据稀疏, 社交信任, 聚类技术, 信任网络

Abstract: Collaborative filtering algorithm is the most widely used algorithm in personalized recommendation system. Focusing on the problem of date sparseness and poor scalability, a new clustering recommendation algorithm based on user interest and social trust was proposed. Firstly, according to user rating information, the algorithm divided users into different categories by clustering technology, and set up a user neighbor set based on interest. In order to improve the accuracy of the calculation of interest similarity, the modified cosine formula was used to eliminate the difference of user scoring criteria. Then, the trust mechanism is introduced to measure implicit trust value among users by defining the direct trust calculation method and indirect trust calculation method, converted a social network to a trust network, and set up a user neighbor set based on trust. Finally, this algorithm combined the predictive value of two neighbor sets to generate recommendations for users by weighting method. The simulation experiment was carried out to test the performance on Douban dataset, found suitable value of α and k. Compared with collaborative filtering algorithm based on users and recommendation algorithm based on trust, the Mean Absolute Error (MAE) decreased by 6.7%, precision, recall and F1 increased by 25%,40% and 37%. The proposed algorithm can effectively improve the quality of recommendation system.

Key words: collaborative filtering, date sparseness, social trust, clustering technique, trust network

中图分类号:

TP391.1

肖晓丽, 钱娅丽, 李旦江, 谭柳斌. 基于用户兴趣和社交信任的聚类推荐算法[J]. 计算机应用, 2016, 36(5): 1273-1278.

XIAO Xiaoli, QIAN Yali, LI Danjiang, TAN Liubin. Clustering recommendation algorithm based on user interest and social trust[J]. Journal of Computer Applications, 2016, 36(5): 1273-1278.

参考文献

[1] CHOI K, SUH Y. A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-Based Systems, 2013, 37:146-153.
[2] KRZYWICKI A, WOBCKE W, KIM Y S, et al. Collaborative filtering for people-to-people recommendation in online dating:data analysis and user trial[J]. International Journal of Human Computer Studies, 2015, 76:50-66.
[3] 施凤仙, 陈恩红.结合项目区分用户兴趣度的协同过滤算法[J].小型微型计算机系统, 2012, 33(7):1533-1536.(SHI F X, CHEN E H.Combining the items' discriminabilities on user interests for collaborative filtering[J]. Journal of Chinese Computer Systems, 2012, 33(7):1533-1536.)
[4] 吴泓辰, 王新军, 成勇, 等.基于协同过滤与划分聚类的改进推荐算法[J].计算机研究与发展, 2011, 48(S3):205-212.(WU H C, WANG X J, CHENG Y, et al. Advanced recommendation based on collaborative filtering and partition clustering[J]. Journal of Computer Research and Development, 2011, 48(S3):205-212.)
[5] 尹航, 常桂然, 王兴伟.采用聚类算法优化的K近邻协同过滤算法[J].小型微型计算机系统, 2013, 34(4):806-809.(YIN H, CHANG G R, WANG X W. Effect of clustering algorithm in K-nearest-neighborhood based collaborative filtering[J]. Journal of Chinese Computer Systems, 2013, 34(4):806-809.)
[6] 王明佳, 韩景倜, 韩松乔.基于模糊聚类的协同过滤算法[J].计算机工程, 2012, 38(24):50-52.(WANG M J, HAN J T, HAN S Q. Collaborative filtering algorithm based on fuzzy clustering[J]. Computer Engineering, 2012, 38(24):50-52.)
[7] 孙辉, 马跃, 杨海波, 等.一种相似度改进的用户聚类协同过滤推荐算法[J].小型微型计算机系统, 2014, 35(9):1967-1970.(SUN H, MA Y, YANG H B, et al. Collaborative filtering recommendation algorithm by optimizing similarity and clustering user[J]. Journal of Chinese Computer Systems, 2014, 35(9):1967-1970.)
[8] 吴湖, 王永吉, 王哲, 等.两阶段联合聚类协同过滤算法[J].软件学报, 2010, 21(5):1042-1054.(WU H, WANG Y J, WANG Z, et al. Two-phase collaborative filtering algorithm based on co-clustering[J]. Journal of Software, 2010, 21(5):1042-1054.)
[9] 王雪蓉, 万年红.云模式用户行为关联聚类的协同过滤推荐算法[J].计算机应用, 2011, 31(9):2421-2425.(WANG X R, WANG N H. Cloud pattern collaborative filtering recommender algorithm using user behavior correlation clustering[J]. Journal of Computer Applications, 2011, 31(9):2421-2425.)
[10] 荣辉桂, 火生旭, 胡春华, 等.基于用户相似度的协同过滤推荐算法[J].通信学报, 2014, 35(2):16-24.(RONG H G, HONG S X, HU C H, et al. User similarity-based collaborative filtering recommendation algorithm[J]. Journal on Communications, 2014, 35(2):16-24.)
[11] REAFEE W, SALIM N. the social network role in improving recommendation performance of collaborative filtering[C]//Proceedings of the 1st International Conference on Advanced Data and Information Engineering. Berlin:Springer, 2014:231-240.
[12] 邹本友, 李翠平, 谭力文, 等.基于用户信任和张量分解的社会网络推荐[J].软件学报, 2014, 25(12):2852-2864.(ZOU B Y, LI C P, TAN L W, et al. Social recommendations based on user trust and tensor factorization[J]. Journal of Software, 2014, 25(12):2852-2864.)
[13] 张富国.用户多兴趣下基于信任的协同过滤算法研究[J].小型微型计算机系统, 2008, 29(8):1415-1419.(ZHANG F G. Research on trust based collaborative filtering algorithm for user's multiple interests[J]. Journal of Chinese Computer Systems, 2008, 29(8):1415-1419.)
[14] 王兴茂, 张兴明, 邬江兴.基于一跳信任模型的协同过滤推荐算法[J].通信学报, 2015, 36(6):193-200.(WANG X M, ZHANG X M, WU J X. Collaborative filtering recommendation algorithm based on one-jump trust model[J]. Journal on Communications, 2015, 36(6):193-200.)
[15] 杨兴耀, 于炯, 吐尔根·依布拉音, 等.基于信任模型填充的协同过滤推荐模型[J].计算机工程, 2015, 41(5):6-13.(YANG X Y, YU J, IBRAHIM T, et al. Collaborative filtering recommendation model based on trust model filling[J]. Computer Engineering, 2015, 41(5):6-13.)
[16] 冯勇, 李军平, 徐红艳, 等.基于社会网络分析的协同推荐方法改进[J].计算机应用, 2013, 33(3):841-844.(FENG Y, LI J P, XU H Y, et al. Collaborative recommendation method improvement based on social network analysis[J]. Journal of Computer Applications, 2013, 33(3):841-844.)
[17] 夏小伍, 王卫平.基于信任模型的协同过滤推荐算法[J]. 计算机工程, 2011, 37(21):26-28.(XIA X W, WANG W P. Collaborative filtering recommendation algorithm based on trust model[J]. Computer Engineering, 2011, 37(21):26-28.)
[18] 周璐璐.融合社会信任关系的改进推荐系统[J].计算机应用与软件, 2014, 31(7):31-35.(ZHOU L L. Improved recommendation system based on social trust relation[J]. Computer Applications and Software, 2014, 31(7):31-35.)

基于用户兴趣和社交信任的聚类推荐算法

Clustering recommendation algorithm based on user interest and social trust

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

[1]	包玄, 陈红梅, 肖清. 融入时间的兴趣点协同推荐算法[J]. 计算机应用, 2021, 41(8): 2406-2411.
[2]	李翔锟, 贾彩燕. 融合重叠社区正则化及隐式反馈的协同过滤方法[J]. 计算机应用, 2021, 41(1): 53-59.
[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]	陈曦, 梅广, 张金金, 许维胜. 融合知识图谱和协同过滤的学生成绩预测方法[J]. 计算机应用, 2020, 40(2): 595-601.
[5]	张文龙, 钱付兰, 陈洁, 赵姝, 张燕平. 基于双重最相关注意力网络的协同过滤推荐算法[J]. 计算机应用, 2020, 40(12): 3445-3450.
[6]	樊玮, 谢聪, 肖春景, 曹淑燕. 基于组合类别空间的随机游走推荐算法[J]. 计算机应用, 2019, 39(4): 984-988.
[7]	雷曼, 龚琴, 王纪超, 王保群. 基于标签权重的协同过滤推荐算法[J]. 计算机应用, 2019, 39(3): 634-638.
[8]	刘彤, 曾诚, 何鹏. 基于用户网络嵌入的民宿房源推荐方法[J]. 计算机应用, 2019, 39(11): 3398-3402.
[9]	许朝, 孟凡荣, 袁冠, 李月娥, 刘肖. 融合地点影响力的兴趣点推荐算法[J]. 计算机应用, 2019, 39(11): 3178-3183.
[10]	徐玲玲, 曲志坚, 徐红博, 曹小威, 刘晓红. 融合信任信息的欧氏嵌入推荐算法[J]. 计算机应用, 2019, 39(10): 2829-2833.
[11]	文凯, 朱传亮. 融合社交网络和兴趣的正则化矩阵分解推荐模型[J]. 计算机应用, 2018, 38(9): 2523-2528.
[12]	乔永卫, 张宇翔, 肖春景. 基于会话时序相似性的矩阵分解数据填充[J]. 计算机应用, 2018, 38(8): 2236-2242.
[13]	杨帅, 王鹃. 基于堆栈降噪自编码器改进的混合推荐算法[J]. 计算机应用, 2018, 38(7): 1866-1871.
[14]	王永贵, 宋真真, 肖成龙. 基于改进聚类和矩阵分解的协同过滤推荐算法[J]. 计算机应用, 2018, 38(4): 1001-1006.
[15]	廖天星, 王玲. 融合项目标签相似性的协同过滤推荐算法[J]. 计算机应用, 2018, 38(4): 1007-1011.