[1]GU Junhua,XIE Zhijian,WU Junyan,et al.Parallel collaborative filtering recommendation algorithm based on graph walk[J].CAAI Transactions on Intelligent Systems,2019,14(4):743-751.[doi:10.11992/tis.201806002]
Copy

Parallel collaborative filtering recommendation algorithm based on graph walk

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 743-751 Column: 学术论文—机器学习 Public date: 2019-07-02
Title:
Parallel collaborative filtering recommendation algorithm based on graph walk
Author(s):
GU Junhua1; 2;  XIE Zhijian1; 2;  WU Junyan1; 2;  XU Xinyun1; 2;  ZHANG Suqi3
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China;
2. Hebei Province Key Laboratory of Big Data Computing, Tianjin 300401, China;
3. School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Keywords:
collaborative filtering; recommendation; user network map; walk; similarity; indirect similarity; parallel; Spark platform
CLC:
TP391
DOI:
10.11992/tis.201806002
Abstract:
This study aims to solve the problem of data sparsity and scalability of collaborative filtering recommendation algorithms. For the sparseness problem, the traditional Pearson correlation similarity is introduced to calculate the direct similarity between the users using the cross-ratio coefficients. This method alleviates the proportion of common scoring items among users. An indirect similarity calculation method based on graph walk is proposed in the paper. This method builds a user network map based on the direct similarity between users, calculates the indirect similarity between users by walking on the user network map, and makes recommendations. The parallelization of this method on the Spark platform mitigates the scalability problem caused by increase of the data size. Experimental results on Movielens dataset and IPTV dataset show that the proposed algorithm achieves good results on different datasets, effectively improves the recommendation accuracy rate, and has good scalability in a distributed environment.
References:
[1] 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):1619-1647. HUANG Liwei, LIU Yanbo, LI Deyi. Deep learning based recommender systems[J].Chinese journal of computers. 2018,41(07):1619-1647.
[2] 孙光福, 吴乐, 刘淇, 等. 基于时序行为的协同过滤推荐算法[J]. 软件学报, 2013, 24(11):2721-2733 SUN Guangfu, WU Le, LIU Qi, et al. Recommendations based on collaborative filtering by exploiting sequential behaviors[J]. Journal of software, 2013, 24(11):2721-2733
[3] 许智宏, 蒋新宇, 董永峰, 等. 一种基于Spark的改进协同过滤算法研究[J]. 计算机应用与软件, 2017, 34(5):247-254, 278 XU Zhihong, JIANG Xinyu, DONG Yongfeng, et al. An improved collaborative filtering algorithm based on Spark[J]. Computer applications and software, 2017, 34(5):247-254, 278
[4] SUN Baoshan, DONG Lingyu. Dynamic model adaptive to user interest drift based on cluster and nearest neighbors[J]. IEEE access, 2017, 5:1682-1691.
[5] 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. Context-aware POI recommendation based on matrix factorization[J]. Chinese Journal of Computers:(2018-05-14)[2018-05-30]. http://kns.cnki.net/kcms/detail/11.1826.TP.20180512.2150.008.html.
[6] WU Jibing, YU Lianfei, ZHANG Qun, et al. Multityped community discovery in time-evolving heterogeneous information networks based on tensor decomposition[J]. Complexity, 2018, 2018:9653404.
[7] MA Wenping, WU Yue, GONG Maoguo, et al. Local probabilistic matrix factorization for personal recommendation[C]//Proceedings of the 13th International Conference on Computational Intelligence and Security. Hong Kong, China, 2017:97-101.
[8] 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 filtering recommendation algorithm[J]. Computer systems and applications, 2013, 22(7):108-112
[9] LU F, HONG L, CHANGFENG L. The improvement and implementation of distributed item-based collaborative filtering algorithm on Hadoop[C]//Proceedings of the 34th Chinese Control Conference. Hangzhou, China, 2015:9078-9083.
[10] KUPISZ B, UNOLD O. Collaborative filtering recommendation algorithm based on Hadoop and Spark[C]//Proceedings of 2015 IEEE International Conference on Industrial Technology. Seville, Spain, 2015:1510-1514.
[11] 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]. Pattern recognition and artificial intelligence, 2014, 27(8):720-734
[12] 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 recommendation 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 personalized web service recommendation based on improved collaborative filtering[J]. Computer technology and development, 2018, 28(1):64-68
[14] WU Xiaokun, CHENG Bo, CHEN Junliang. Collaborative filtering service recommendation based on a novel similarity computation method[J]. IEEE transactions on services computing, 2017, 10(3):352-365.
[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[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 using random walk[J]. Application research of computers, 2018, 35(8):2304-2307
[16] 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 recommenddation algorithm based on graph structure and item type[J]. Data communications, 2016(5):44-47
[17] 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 propagation model and algorithm for influence maximization in social network based on pageRank[J]. Computer science, 2013, 40(S1):136-140
[18] HU Yan, PENG Qimin, HU Xiaohui, et al. Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering[J]. IEEE transactions 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 reconstruction of level set function[J]. Journal of Harbin Engineering University, 2017, 38(6):836-842
[20] LIU Tiantian, FANG Zhiyi, ZHAO Chen, et al. Parallelization 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.
[21] 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 implicit scoring model based on Hadoop[J]. Journal of computer applications, 2017, 37(11):3188-3193
Similar References:

Memo

-

Last Update: 2019-08-25

Copyright © CAAI Transactions on Intelligent Systems