基于蜂群K-means聚类模型的协同过滤推荐算法
|
摘要
针对目前协同过滤推荐算法的推荐质量和推荐效率低的问题,提出了一种基于改进蜂群K-means聚类模型的协同过滤推荐算法。首先,根据用户属性信息,采用改进蜂群K-means算法对用户进行聚类,建立用户聚类模型;然后,计算目标用户与用户聚类模型中各聚类中心的距离,其中距离最近的类为目标用户的检索空间;最后,从检索空间中依据用户-项目评分矩阵通过相似度计算搜索目标用户的最近邻居,由最近邻居的信息产生推荐列表。实验结果表明,该算法降低了平均绝对误差值,缩短了运行时间,提高了推荐质量和推荐效率。
To address the problem of low recommendation quality and low recommendation efficiency of current collaborative filtering recommendation algorithms, we propose a collaborative filtering recommendation algorithm based on an improved bee colony K-means clustering model. Firstly, based on user attribute information, the algorithm uses the improved bee colony K-means algorithm to cluster users and establish a user clustering model. Secondly, we calculate the distance between target users and the clustering center in the user clustering model, and the cluster with the minimal distance is taken as the retrieval space of active users. Finally, we search the nearest neighbor of the target user by the similarity calculation according to the user-item scoring matrix, and generate a recommendation list via the information of the nearest neighbor. Experimental results show that the proposed algorithm can achieve lower MAE and shorter running time, and it can enhance the quality and efficiency of recommendation.
To address the problem of low recommendation quality and low recommendation efficiency of current collaborative filtering recommendation algorithms, we propose a collaborative filtering recommendation algorithm based on an improved bee colony K-means clustering model. Firstly, based on user attribute information, the algorithm uses the improved bee colony K-means algorithm to cluster users and establish a user clustering model. Secondly, we calculate the distance between target users and the clustering center in the user clustering model, and the cluster with the minimal distance is taken as the retrieval space of active users. Finally, we search the nearest neighbor of the target user by the similarity calculation according to the user-item scoring matrix, and generate a recommendation list via the information of the nearest neighbor. Experimental results show that the proposed algorithm can achieve lower MAE and shorter running time, and it can enhance the quality and efficiency of recommendation.
引文
[1] Hou Y,Tang Y,Wang S.A collaborative filtering recommendation based on user trust and common liking rate[C]//Proc of the 2nd International Conference on Digital Signal Processing,2018:186-190.
[2] Nilashi M,Esfahani M D,Roudbaraki M Z,et al.A multi-criteria collaborative filtering recommender system using clustering and regression techniques[J].Journal of Soft Computing and Decision Support Systems,2016,3(5):24-30.
[3] Xiao Wen-qiang,Yao Shi-jun,Wu Shan-ming.Improved top-N collaborative filtering recommendation algorithm[J].Application Research of Computers,2018,35(1):105-108.(in Chinese)
[4] Zhang Li,Qin Tao,Teng Pi-qiang.An improved collaborative filtering algorithm based on user clustering[J].Information Science,2014,32(10):24-27.(in Chinese)
[5] Cheng Guang-hua.A collaborative filtering algorithm based on user clustering in E-commerce personalized systems [J].Advanced Materials Research,2011,267:789-793.
[6] Yang Xing-yu,Li Hua-ping,Zhang Yu-bo.Collaborative filtering recommendation algorithm based on clustering and random forest[J].Computer Engineering and Applications,2018,54(16):152-157.(in Chinese)
[7] Tian Bao-jun,Hu Pei-pei,Du Xiao-juan,et al.Optimization of the collaborative filtering recommendation algorithm based on clustering under Hadoop[J].Computer Engineering & Science,2016,38(8):1615-1624.(in Chinese)
[8] Ma Xiao,Lu Hong-wei,Gan Zao-bin,et al.An explicit trust and distrust clustering based collaborative filtering recommendation approach[J].Electronic Commerce Research and Applications,2017,25(C):29-39.
[9] Wang Xing-mao,Zhang Xing-ming,Wu Yi-tao.A collaborative recommendation algorithm based on heuristic clustering model and category similarity[J].Acta Electronica Sinica,2016,44(7):1708-1713.(in Chinese)
[10] Shinde S K,Kulkarni U V.Hybrid personalized recommender system using fast K-medoids clustering algorithm[J].Journal of Advances in Information Technology,2011,2(3):152-158.
[11] Wen J,Zhou W.An improved item-based collaborative filtering algorithm based on clustering method[J].Journal of Computational Information Systems,2012,8(2):571-578.
[12] Sun Tian-hao,Li An-neng,Li Ming.Study on distributed improved clustering collaborative filtering algorithm based on Hadoop [J].Computer Engineering and Applications,2015,51(15):124-128.(in Chinese)
[13] Xiong Qin-qiao.Research on personalized recommendation method based on user clustering [D].Nanchang:Jiangxi University of Finance and Economics,2017.(in Chinese)
[14] Liu Xiao-jun.An improved clustering-based collaborative filtering recommendation algorithm[J].Cluster Computing,2017,20(2):1281-1288.
[15] Moradi P,Ahmadian S,Akhlaghian F.An effective trust-based recommendation method using a novel graph clustering algorithm[J].Physica A:Statistical Mechanics & Its Applications,2015,436:462-481.
[16] Xie Juan-ying,Wang Yan-e.K-means algorithm based on minimum deviation initialized clustering centers[J].Computer Engineering,2014,40(8):205-211.(in Chinese)
[17] Xiong Zhong-yang,Chen Ruo-tian,Zhang Yu-fang.Effective method for centers initialization in K-means clustering[J].Application Research of Computers,2011,28(11):4188-4190.(in Chinese)
[18] Hou Yue.Improved multi-cardiac location selection based on maximum and minimum distance clustering algorithm [D].Dalian:Liaoning Normal University,2015.(in Chinese)
[19] He Si-yun,Gao Jian-ling,Chen Lan.K-means clustering algorithm based on improved artificial bee colony algorithm[J].Journal of Guizhou University(Natural Science),2017,34(5):83-87.(in Chinese)
[20] Zhou Kai-le,Yang Shan-lin,Ding Shuai,et al.On clustering vilidation[J].Systems Engineering Theory & Practice,2014,34(9):2417-2431.(in Chinese)
[21] Piao Shang-zhe,Chaomurilige,Yu Jian.Cluster validity indexs for FCM clustering algorithm[J].Pattern Recognition and Artificial Intelligence,2015,28(5):452-461.(in Chinese)
[22] Ma Xing-min.Research and implementation of collaborative recommendation based on ABC algorithm[D].Qingdao:China University of Petroleum (East China),2014.(in Chinese)
[23] Ungar L H,Foster D P.Clustering methods for collaborative filtering[C]//Proc of AAAI Workshop on Recommendation Systems,1998:114-129.
[24] He Ming,Sun Wang,Xiao Run,et al.Collaborative filtering recommendation algorithm combining clustering and user preferences[J].Computer Science,2017,44(IIA):391-396.(in Chinese)
[25] Liu Hui.Research on improvement of user-based collaborative filtering recommendation algorithm [D].Xiamen:Huaqiao University,2016.(in Chinese)
[26] Zha Wen-qin.Collaborative filtering recommendation based on user clustering for item’s multiple contents[D].Hefei:Hefei University of Technology,2009.(in Chinese)
[3] 肖文强,姚世军,吴善明.一种改进的top-N协同过滤推荐算法[J].计算机应用研究,2018,35(1):105-108.
[4] 张莉,秦桃,滕丕强.一种改进的基于用户聚类的协同过滤算法[J].情报科学,2014,32(10):24-27.
[6] 杨兴雨,李华平,张宇波.基于聚类和随机森林的协同过滤推荐算法[J].计算机工程与应用,2018,54(16):152-157.
[7] 田保军,胡培培,杜晓娟,等.Hadoop下基于聚类协同过滤推荐算法优化的研究[J].计算机工程与科学,2016,38(8):1615-1624.
[9] 王兴茂,张兴明,吴毅涛.基于启发式聚类模型和类别相似度的协同过滤推荐算法[J].电子学报,2016,44(7):1708-1713.
[12] 孙天昊,黎安能,李明.基于 Hadoop 分布式改进聚类协同过滤推荐算法研究[J].计算机工程与应用,2015,51(15):124-128.
[13] 熊琴巧.基于用户聚类的个性化推荐方法研究[D].南昌:江西财经大学,2017.
[16] 谢娟英,王艳娥.最小方差优化初始聚类中心的K-means算法[J].计算机工程,2014,40(8):205-211.
[17] 熊忠阳,陈若田,张玉芳.一种有效的K-means聚类中心初始化方法[J].计算机应用研究,2011,28(11):4188-4190.
[18] 侯玥.基于最大最小距离聚类算法的改进多重心法选址研究[D].大连:辽宁师范大学,2015.
[19] 贺思云,高建瓴,陈岚.基于改进人工蜂群算法的k-means聚类算法[J].贵州大学学报(自然科学版),2017,34(5):83-87.
[20] 周开乐,杨善林,丁帅,等.聚类有效性研究综述[J].系统工程理论与实践,2014,34(9):2417-2431.
[21] 朴尚哲,超木日力格,于剑.模糊C均值算法的聚类有效性评价[J].模式识别与人工智能,2015,28(5):452-461.
[22] 马兴敏.基于蜂群算法的协同过滤推荐系统的研究与实现[D].青岛:中国石油大学(华东),2014.
[24] 何明,孙望,肖润,等.一种融合聚类与用户兴趣偏好的协同过滤推荐算法[J].计算机科学,2017,44(IIA):391-396.
[25] 刘辉.基于用户的协同过滤推荐算法的改进研究[D].厦门:华侨大学,2016.
[26] 查文琴.基于用户聚类的项目多内容协同过滤推荐[D].合肥:合肥工业大学,2009.
[2] Nilashi M,Esfahani M D,Roudbaraki M Z,et al.A multi-criteria collaborative filtering recommender system using clustering and regression techniques[J].Journal of Soft Computing and Decision Support Systems,2016,3(5):24-30.
[3] Xiao Wen-qiang,Yao Shi-jun,Wu Shan-ming.Improved top-N collaborative filtering recommendation algorithm[J].Application Research of Computers,2018,35(1):105-108.(in Chinese)
[4] Zhang Li,Qin Tao,Teng Pi-qiang.An improved collaborative filtering algorithm based on user clustering[J].Information Science,2014,32(10):24-27.(in Chinese)
[5] Cheng Guang-hua.A collaborative filtering algorithm based on user clustering in E-commerce personalized systems [J].Advanced Materials Research,2011,267:789-793.
[6] Yang Xing-yu,Li Hua-ping,Zhang Yu-bo.Collaborative filtering recommendation algorithm based on clustering and random forest[J].Computer Engineering and Applications,2018,54(16):152-157.(in Chinese)
[7] Tian Bao-jun,Hu Pei-pei,Du Xiao-juan,et al.Optimization of the collaborative filtering recommendation algorithm based on clustering under Hadoop[J].Computer Engineering & Science,2016,38(8):1615-1624.(in Chinese)
[8] Ma Xiao,Lu Hong-wei,Gan Zao-bin,et al.An explicit trust and distrust clustering based collaborative filtering recommendation approach[J].Electronic Commerce Research and Applications,2017,25(C):29-39.
[9] Wang Xing-mao,Zhang Xing-ming,Wu Yi-tao.A collaborative recommendation algorithm based on heuristic clustering model and category similarity[J].Acta Electronica Sinica,2016,44(7):1708-1713.(in Chinese)
[10] Shinde S K,Kulkarni U V.Hybrid personalized recommender system using fast K-medoids clustering algorithm[J].Journal of Advances in Information Technology,2011,2(3):152-158.
[11] Wen J,Zhou W.An improved item-based collaborative filtering algorithm based on clustering method[J].Journal of Computational Information Systems,2012,8(2):571-578.
[12] Sun Tian-hao,Li An-neng,Li Ming.Study on distributed improved clustering collaborative filtering algorithm based on Hadoop [J].Computer Engineering and Applications,2015,51(15):124-128.(in Chinese)
[13] Xiong Qin-qiao.Research on personalized recommendation method based on user clustering [D].Nanchang:Jiangxi University of Finance and Economics,2017.(in Chinese)
[14] Liu Xiao-jun.An improved clustering-based collaborative filtering recommendation algorithm[J].Cluster Computing,2017,20(2):1281-1288.
[15] Moradi P,Ahmadian S,Akhlaghian F.An effective trust-based recommendation method using a novel graph clustering algorithm[J].Physica A:Statistical Mechanics & Its Applications,2015,436:462-481.
[16] Xie Juan-ying,Wang Yan-e.K-means algorithm based on minimum deviation initialized clustering centers[J].Computer Engineering,2014,40(8):205-211.(in Chinese)
[17] Xiong Zhong-yang,Chen Ruo-tian,Zhang Yu-fang.Effective method for centers initialization in K-means clustering[J].Application Research of Computers,2011,28(11):4188-4190.(in Chinese)
[18] Hou Yue.Improved multi-cardiac location selection based on maximum and minimum distance clustering algorithm [D].Dalian:Liaoning Normal University,2015.(in Chinese)
[19] He Si-yun,Gao Jian-ling,Chen Lan.K-means clustering algorithm based on improved artificial bee colony algorithm[J].Journal of Guizhou University(Natural Science),2017,34(5):83-87.(in Chinese)
[20] Zhou Kai-le,Yang Shan-lin,Ding Shuai,et al.On clustering vilidation[J].Systems Engineering Theory & Practice,2014,34(9):2417-2431.(in Chinese)
[21] Piao Shang-zhe,Chaomurilige,Yu Jian.Cluster validity indexs for FCM clustering algorithm[J].Pattern Recognition and Artificial Intelligence,2015,28(5):452-461.(in Chinese)
[22] Ma Xing-min.Research and implementation of collaborative recommendation based on ABC algorithm[D].Qingdao:China University of Petroleum (East China),2014.(in Chinese)
[23] Ungar L H,Foster D P.Clustering methods for collaborative filtering[C]//Proc of AAAI Workshop on Recommendation Systems,1998:114-129.
[24] He Ming,Sun Wang,Xiao Run,et al.Collaborative filtering recommendation algorithm combining clustering and user preferences[J].Computer Science,2017,44(IIA):391-396.(in Chinese)
[25] Liu Hui.Research on improvement of user-based collaborative filtering recommendation algorithm [D].Xiamen:Huaqiao University,2016.(in Chinese)
[26] Zha Wen-qin.Collaborative filtering recommendation based on user clustering for item’s multiple contents[D].Hefei:Hefei University of Technology,2009.(in Chinese)
[3] 肖文强,姚世军,吴善明.一种改进的top-N协同过滤推荐算法[J].计算机应用研究,2018,35(1):105-108.
[4] 张莉,秦桃,滕丕强.一种改进的基于用户聚类的协同过滤算法[J].情报科学,2014,32(10):24-27.
[6] 杨兴雨,李华平,张宇波.基于聚类和随机森林的协同过滤推荐算法[J].计算机工程与应用,2018,54(16):152-157.
[7] 田保军,胡培培,杜晓娟,等.Hadoop下基于聚类协同过滤推荐算法优化的研究[J].计算机工程与科学,2016,38(8):1615-1624.
[9] 王兴茂,张兴明,吴毅涛.基于启发式聚类模型和类别相似度的协同过滤推荐算法[J].电子学报,2016,44(7):1708-1713.
[12] 孙天昊,黎安能,李明.基于 Hadoop 分布式改进聚类协同过滤推荐算法研究[J].计算机工程与应用,2015,51(15):124-128.
[13] 熊琴巧.基于用户聚类的个性化推荐方法研究[D].南昌:江西财经大学,2017.
[16] 谢娟英,王艳娥.最小方差优化初始聚类中心的K-means算法[J].计算机工程,2014,40(8):205-211.
[17] 熊忠阳,陈若田,张玉芳.一种有效的K-means聚类中心初始化方法[J].计算机应用研究,2011,28(11):4188-4190.
[18] 侯玥.基于最大最小距离聚类算法的改进多重心法选址研究[D].大连:辽宁师范大学,2015.
[19] 贺思云,高建瓴,陈岚.基于改进人工蜂群算法的k-means聚类算法[J].贵州大学学报(自然科学版),2017,34(5):83-87.
[20] 周开乐,杨善林,丁帅,等.聚类有效性研究综述[J].系统工程理论与实践,2014,34(9):2417-2431.
[21] 朴尚哲,超木日力格,于剑.模糊C均值算法的聚类有效性评价[J].模式识别与人工智能,2015,28(5):452-461.
[22] 马兴敏.基于蜂群算法的协同过滤推荐系统的研究与实现[D].青岛:中国石油大学(华东),2014.
[24] 何明,孙望,肖润,等.一种融合聚类与用户兴趣偏好的协同过滤推荐算法[J].计算机科学,2017,44(IIA):391-396.
[25] 刘辉.基于用户的协同过滤推荐算法的改进研究[D].厦门:华侨大学,2016.
[26] 查文琴.基于用户聚类的项目多内容协同过滤推荐[D].合肥:合肥工业大学,2009.