正在加载图片...
第4期 顾军华,等:基于图游走的并行协同过滤推荐算法 ·751· [4]SUN Baoshan,DONG Lingyu.Dynamic model adaptive to 2018,35(8):23042307 user interest drift based on cluster and nearest neighbors[J]. [16]王鹤,邬春学.基于图结构和项目类型的协同过滤推荐 IEEE access.2017,5:1682-1691 算法[J.数据通信,2016(5):44-47. [5]彭宏伟,靳远远,吕晓强,等.一种基于矩阵分解的上下 WANG He,WU Chunxue.Collaborative filtering recom- 文感知POI推荐算法J/OL1.计算机学报:(2018-05 menddation algorithm based on graph structure and item 14)[2018-05-301.http://kns.cnki.net/kcms/detail/11.1826.TP. type[J].Data communications,2016(5):44-47. 20180512.2150.008.html. [17]宫秀文,张佩云.基于PageRank的社交网络影响最大 PENG Hongwei,JIN Yuanyuan,LU Xiaogiang,et al.Con- 化传播模型与算法研究几.计算机科学,2013,40(S1): text-aware POI recommendation based on matrix factoriza- 136-140. tion[J].Chinese Journal of Computers:(2018-05-14)[2018- GONG Xiuwen,ZHANG Peiyun.Research on propaga- 05-301.http://kns.cnki.net/kcms/detail/11.1826.TP.20180512. tion model and algorithm for influence maximization in 2150.008.html social network based on pageRank[J].Computer science, [6]WU Jibing,YU Lianfei,ZHANG Qun,et al.Multityped 2013,40(S1136-140 community discovery in time-evolving heterogeneous in- [18]HU Yan,PENG Qimin,HU Xiaohui,et al.Time aware formation networks based on tensor decomposition[J]. Complexity,2018,2018:9653404. and data sparsity tolerant web service recommendation based on improved collaborative filtering[J].IEEE trans- [7]MA Wenping,WU Yue,GONG Maoguo,et al.Local probabilistic matrix factorization for personal recommend- actions on services computing,2015,8(5):782-794. [19]黄筱云,董国海,常佳夫,等.Level set函数快速步进重 ation[C]//Proceedings of the 13th International Conference 构并行算法的改进[J].哈尔滨工程大学学报,2017 on Computational Intelligence and Security.Hong Kong, 38(6:836-842 China.2017:97-101. [8]杨志文,刘波.基于Hadoop平台协同过滤推荐算法仞. HUANG Xiaoyun,DONG Guohuai,CHANG Jiafu,et al. 计算机系统应用,2013,22(7):108-112 Improvement of parallel fast marching method for recon- YANG Zhiwen,LIU Bo.Hadoop-based collaborative fil- struction of level set function[J].Journal of Harbin Engin- tering recommendation algorithm[J].Computer systems eering University,2017,38(6):836-842. and applications,2013,22(7):108-112. [20]LIU Tiantian,FANG Zhiyi,ZHAO Chen,et al.Paralleliz- [9]LU F.HONG L.CHANGFENG L.The improvement and ation of a series of extreme learning machine algorithms implementation of distributed item-based collaborative fil- based on spark[C]//Proceedings of the IEEE/ACIS 15th tering algorithm on Hadoop[C]//Proceedings of the 34th International Conference on Computer and Information Chinese Control Conference.Hangzhou,China,2015: Science.Okayama,Japan,2016. 9078-9083 [21]顾军华,官磊,张建,等.基于Hadoop的PTV隐式评分 [10]KUPISZ B,UNOLD O.Collaborative filtering recom- 模型].计算机应用,2017,37(11):3188-3193 mendation algorithm based on Hadoop and Spark[C]// GU Junhua,GUAN Lei,ZHANG Jian,et al.IPTV impli- Proceedings of 2015 IEEE International Conference on cit scoring model based on Hadoop[J].Journal of com Industrial Technology.Seville,Spain,2015:1510-1514. puter applications,2017,37(11):3188-3193. [11]冷亚军,陆青,梁昌勇.协同过滤推荐技术综述).模式 识别与人工智能,2014,27(8):720-734. 作者简介: LENG Yajun,LU Qing,LIANG Changyong.Survey of 顾军华,男,1966年生,教授,博 recommendation based on collaborative filtering[J].Pat- 士生导师,CC℉会员,中国离散数学学 tern recognition and artificial intelligence,2014,27(8): 会常务理事,河北省计算机学会副理 720-734. 事长。主要研究方向为数据挖掘、智 [12]范波,程久军.用户间多相似度协同过滤推荐算法) 能信息处理等。完成科研项目30余 计算机科学,2012,391少:23-26 项,发表学术论文50余篇。 FAN Bo.CHENG Jiujun.Collaborative filtering recom- mendation algorithm based on user's multi-similarity[J]. Computer Science,2012,39(1):23-26. [13]徐堃,朱小柯,荆晓远.基于改进协同过滤的个性化 谢志坚,男,1995年生,硕士研究 web服务推荐方法研究].计算机技术与发展,2018, 生,主要研究方向为数据挖掘与机器 28(1:64-68. 学习。 XU Kun,ZHU Xiaoke,JING Xiaoyuan.Research on per- sonalized web service recommendation based on im- proved collaborative filtering[J].Computer technology and development,2018,28(1):64-68. [14]WU Xiaokun,CHENG Bo,CHEN Junliang.Collaborat- ive filtering service recommendation based on a novel 武君艳,女,1994年生,硕士研究 similarity computation method[J].IEEE transactions on 生,主要研究方向为数据挖掘与计算 services computing,2017,10(3):352-365. 机仿真。 [15]肖春景,夏克文,乔永卫.基于时序逆影响的随机游走 推荐算法).计算机应用研究,2018,35(8)2304-2307. XIAO Chunjing,XIA Kewen,QIAO Yongwei.Temporal inverse influence based recommendation method by us- ing random walk[J].Application research of computers,SUN Baoshan, DONG Lingyu. Dynamic model adaptive to user interest drift based on cluster and nearest neighbors[J]. IEEE access, 2017, 5: 1682–1691. [4] 彭宏伟, 靳远远, 吕晓强, 等. 一种基于矩阵分解的上下 文感知 POI 推荐算法 [J/OL]. 计算机学报: (2018-05- 14)[2018-05-30]. http://kns.cnki.net/kcms/detail/11.1826.TP. 20180512.2150.008.html. PENG Hongwei, JIN Yuanyuan, LÜ Xiaoqiang, et al. Con￾text-aware POI recommendation based on matrix factoriza￾tion[J]. Chinese Journal of Computers: (2018-05-14)[2018- 05-30]. http://kns.cnki.net/kcms/detail/11.1826.TP.20180512. 2150.008.html. [5] WU Jibing, YU Lianfei, ZHANG Qun, et al. Multityped community discovery in time-evolving heterogeneous in￾formation networks based on tensor decomposition[J]. Complexity, 2018, 2018: 9653404. [6] MA Wenping, WU Yue, GONG Maoguo, et al. Local probabilistic matrix factorization for personal recommend￾ation[C]//Proceedings of the 13th International Conference on Computational Intelligence and Security. Hong Kong, China, 2017: 97–101. [7] 杨志文, 刘波. 基于 Hadoop 平台协同过滤推荐算法 [J]. 计算机系统应用, 2013, 22(7): 108–112. YANG Zhiwen, LIU Bo. Hadoop-based collaborative fil￾tering recommendation algorithm[J]. Computer systems and applications, 2013, 22(7): 108–112. [8] LU F, HONG L, CHANGFENG L. The improvement and implementation of distributed item-based collaborative fil￾tering algorithm on Hadoop[C]//Proceedings of the 34th Chinese Control Conference. Hangzhou, China, 2015: 9078–9083. [9] KUPISZ B, UNOLD O. Collaborative filtering recom￾mendation algorithm based on Hadoop and Spark[C]// Proceedings of 2015 IEEE International Conference on Industrial Technology. Seville, Spain, 2015: 1510–1514. [10] 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 Yajun, LU Qing, LIANG Changyong. Survey of recommendation based on collaborative filtering[J]. Pat￾tern recognition and artificial intelligence, 2014, 27(8): 720–734. [11] 范波, 程久军. 用户间多相似度协同过滤推荐算法 [J]. 计算机科学, 2012, 39(1): 23–26. FAN Bo, CHENG Jiujun. Collaborative filtering recom￾mendation algorithm based on user’s multi-similarity[J]. Computer Science, 2012, 39(1): 23–26. [12] 徐堃, 朱小柯, 荆晓远. 基于改进协同过滤的个性化 web 服务推荐方法研究 [J]. 计算机技术与发展, 2018, 28(1): 64–68. XU Kun, ZHU Xiaoke, JING Xiaoyuan. Research on per￾sonalized web service recommendation based on im￾proved collaborative filtering[J]. Computer technology and development, 2018, 28(1): 64–68. [13] WU Xiaokun, CHENG Bo, CHEN Junliang. Collaborat￾ive filtering service recommendation based on a novel similarity computation method[J]. IEEE transactions on services computing, 2017, 10(3): 352–365. [14] 肖春景, 夏克文, 乔永卫. 基于时序逆影响的随机游走 推荐算法 [J]. 计算机应用研究, 2018, 35(8): 2304–2307. XIAO Chunjing, XIA Kewen, QIAO Yongwei. Temporal inverse influence based recommendation method by us￾ing random walk[J]. Application research of computers, [15] 2018, 35(8): 2304–2307. 王鹤, 邬春学. 基于图结构和项目类型的协同过滤推荐 算法 [J]. 数据通信, 2016(5): 44–47. WANG He, WU Chunxue. Collaborative filtering recom￾menddation algorithm based on graph structure and item type[J]. Data communications, 2016(5): 44–47. [16] 宫秀文, 张佩云. 基于 PageRank 的社交网络影响最大 化传播模型与算法研究 [J]. 计算机科学, 2013, 40(S1): 136–140. GONG Xiuwen, ZHANG Peiyun. Research on propaga￾tion model and algorithm for influence maximization in social network based on pageRank[J]. Computer science, 2013, 40(S1): 136–140. [17] HU Yan, PENG Qimin, HU Xiaohui, et al. Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering[J]. IEEE trans￾actions on services computing, 2015, 8(5): 782–794. [18] 黄筱云, 董国海, 常佳夫, 等. Level set 函数快速步进重 构并行算法的改进 [J]. 哈尔滨工程大学学报, 2017, 38(6): 836–842. HUANG Xiaoyun, DONG Guohuai, CHANG Jiafu, et al. Improvement of parallel fast marching method for recon￾struction of level set function[J]. Journal of Harbin Engin￾eering University, 2017, 38(6): 836–842. [19] LIU Tiantian, FANG Zhiyi, ZHAO Chen, et al. Paralleliz￾ation of a series of extreme learning machine algorithms based on spark[C]//Proceedings of the IEEE/ACIS 15th International Conference on Computer and Information Science. Okayama, Japan, 2016. [20] 顾军华, 官磊, 张建, 等. 基于 Hadoop 的 IPTV 隐式评分 模型 [J]. 计算机应用, 2017, 37(11): 3188–3193. GU Junhua, GUAN Lei, ZHANG Jian, et al. IPTV impli￾cit scoring model based on Hadoop[J]. Journal of com￾puter applications, 2017, 37(11): 3188–3193. [21] 作者简介: 顾军华,男,1966 年生,教授,博 士生导师,CCF 会员,中国离散数学学 会常务理事,河北省计算机学会副理 事长。主要研究方向为数据挖掘、智 能信息处理等。完成科研项目 30 余 项,发表学术论文 50 余篇。 谢志坚,男,1995 年生,硕士研究 生,主要研究方向为数据挖掘与机器 学习。 武君艳,女,1994 年生,硕士研究 生,主要研究方向为数据挖掘与计算 机仿真。 第 4 期 顾军华,等:基于图游走的并行协同过滤推荐算法 ·751·
<<向上翻页
©2008-现在 cucdc.com 高等教育资讯网 版权所有