Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 21-27,48.doi: 10.6040/j.issn.1672-3961.0.2019.411

• Machine Learning & Data Mining • Previous Articles     Next Articles

GRU-based collaborative filtering recommendation algorithm with active learning

Delei CHEN1(),Cheng WANG1,*(),Jianwei CHEN2,Yiyin WU1   

  1. 1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, Fujian, China
    2. Department of Mathematics and Statistics, San Diego State University, San Diego 92182, CA, USA
  • Received:2019-01-03 Online:2020-02-20 Published:2020-02-14
  • Contact: Cheng WANG E-mail:18013083003@hqu.edu.cn;wangcheng@hqu.edu.cn
  • Supported by:
    福建省引导性科技计划资助项目(2017H01010065)

RichHTML

17

PDF (PC)

530

Abstract:

The traditional collaborative filtering recommendation algorithm failed to reflect short-term user interest. In order to reflect the short-term interests of users better, a collaborative filtering recommendation algorithm based on Gated Recurrent Unit (GRU) neural network with active learning was proposed. Based on the GRU neural network, the algorithm processed the data into time-series data to reflect the change of the user's interest and used active learning to sample high-quality data dynamically for accelerating the training of GRU neural network. The result on MovieLens1M dataset showed that the GRU model with active learning could obtain higher short-term prediction success rate, recall rate, item coverage, and user coverage compared with the user-based collaborative filtering method (UCF), the markovian chain model-based collaborative filtering method (MC) and the matrix factory-based collaborative filtering method (LFM), so it could effectively predict the short-term interest of users, improve the accuracy, discover the long-tail items. Meanwhile, it could achieve the same effect with fewer iterations compared with the original GRU model.

Key words: collaborative filtering, gated recurrent unit, active learning, deep learning, time-series data

CLC Number: 

  • TP311

Fig.1

Algorithm flow chart"

Table 1

Comparison of GRU+Active with other recommendation algorithms"

指标 UCF MC LFM GRU+Active
使用数据 原始评分矩阵 时序化评分数据 原始评分矩阵 时序化评分数据
反映短时用户兴趣 差 一般 差 好
短时预测精度 低 一般 低 高
发掘长尾物品能力 差 一般 差 好

Table 2

Parameters values used in the GRU Modeland the GRU+Active Model"

方法 参数
GRU+随机采样 l=0.05, k=50, adam
GRU+Active a=100, l=0.05, k=50, adam

Table 3

The predictive performance of each algorithm on movielens dataset"

方法 Sps/% Recall/% Item-coverage/个 User-coverage/%
UCF 11.62 5.77 301 77.99
LFM 11.28 5.65 399 80.80
MC 19.54 5.27 558 78.70
GRU 32.16 8.85 659 87.98
GRU+Active 32.51 8.86 662 88.81

Fig.2

Comparison of GRU and GRU+Active in Sps"

Fig.3

Comparison of GRU and GRU+Active in recall"

Fig.4

Comparison of GRU and GRU+Active in user coverage"

Fig.5

Comparison of GRU and GRU+Active in item coverage"

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.
doi: 10.3969/j.issn.1003-6059.2014.08.007
LENG Yajun , LU Qing , LIANG Changyong . Survey of recommendation based on collaborative filtering[J]. Pattern Recognition and Artificial Intelligence, 2014, 27 (8): 720- 734.
doi: 10.3969/j.issn.1003-6059.2014.08.007
2 翁小兰, 王志坚. 协同过滤推荐算法研究进展[J]. 计算机工程与应用, 2018, 54 (1): 25- 31.
WENG Xiaolan , WANG Zhijian . Research progress of collaborative filtering recommendation algorithm[J]. Computer Engineering and Applications, 2018, 54 (1): 25- 31.
3 KOREN Y , BELL R . Recommender systems handbook[M]. New York, USA: Springer, 2015: 77- 118.
4 THORAT P B , GOUDAR R M , BARVE S . Survey on collaborative filtering, content-based filtering and hybrid recommendation system[J]. International Journal of Computer Applications, 2015, 110 (4): 31- 36.
doi: 10.5120/19308-0760
5 ELAHI M , RICCI F , RUBENS N . A survey of active learning in collaborative filtering recommender systems[J]. Computer Science Review, 2016, 20, 29- 50.
doi: 10.1016/j.cosrev.2016.05.002
6 季芸, 胡雪蕾. 基于Baseline SVD主动学习算法的推荐系统[J]. 现代电子技术, 2015, 38 (12): 8- 11.
JI Yun , HU Xuelei . Recommender system based on Baseline SVD active learning algorithm[J]. Modern Electronics Technique, 2015, 38 (12): 8- 11.
7 余天豪.基于社会网络的主动信息推送算法研究[D].杭州:杭州师范大学, 2012.
YU Tianhao. Research on recommendation algorithm based on social network[D]. Hangzhou: Hangzhou Normal University, 2012.
8 GUO G, ZHANG J, YORKE-SMITH N. Trust SVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Texas, USA: AAAI Press, 2015: 123-129.
9 JOHNSON J, NG Y K. Enhancing long tail item recommendations using tripartite graphs and Markov process[C]//Proceedings of the International Conference on Web Intelligence. California, USA: ACM, 2017: 761-768.
10 ALSHAMMARI G, JORRO-ARAGONESES J L, KAPETANAKIS S, et al. A hybrid CBR approach for the long tail problem in recommender systems[C]// International Conference on Case-Based Reasoning. Trondheim, Norway: Springer, 2017: 35-45.
11 HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[C]// Proceedings of the 26th International Conference on World Wide Web. Perth, Australia: ACM, 2017: 173-182.
12 XUE H J, DAI X Y, ZHANG J, et al. Deep matrix factorization models for recommender systems[C]//International Joint Conference on Artificial Intelligence. Melbourne, Australia: AAAI Press, 2017: 3203-3209.
13 CHEN T, SUN Y, SHI Y, et al. On sampling strategies for neural network-based collaborative filtering[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax, Canada: ACM, 2017: 767-776.
14 BILLSUS D, PAZZANI M J. Learning collaborative information filters[C]// Proceedings of the Fifteenth International Conference on Machine Learning. California, USA: Morgan Kaufmann Publishers Inc., 1998: 46-54.
15 FUNK S. Netflix update: try this at home[EB/OL]. (2006-12-11) [2019-04-12]. http://sifter.org/~simon/journal/20061211.html.
16 KOREN Y . Factor in the neighbors: scalable and accurate collaborative filtering[J]. Acm Transactions on Knowledge Discovery from Data, 2010, 4 (1): 1- 24.
17 DEVOOGHT R, BERSINI H. Long and short-term recommendations with recurrent neural networks[C]//Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. Bratislava, Slovakia: ACM, 2017: 13-21.
18 LIU J , WU C , WANG J . Gated recurrent units based neural network for time heterogeneous feedback recommendation[J]. Information Sciences, 2018, 423, 50- 65.
doi: 10.1016/j.ins.2017.09.048
19 HOCHREITER S , SURHONE J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780.
doi: 10.1162/neco.1997.9.8.1735
20 CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar: MIT Press, 2014: 1724-1734.
21 HIDASI B, KARATZOGLOU A. Recurrent neural networks with top-k gains for session-based recommendations[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino, Italy:ACM, 2018: 843-852.
22 CHAKRABORTY S , BALASUBRAMANIAN V , SUN Q . Active batch selection via convex relaxations with guaranteed solution bounds[J]. IEEE Trans Pattern Anal Mach Intell, 2015, 37 (10): 1945- 1958.
doi: 10.1109/TPAMI.2015.2389848
23 KONYUSHKOVA K, SZNITMAN R, FUA P. Learning active learning from data[C]//Advances in Neural Information Processing Systems. California, USA: ACM, 2017: 4225-4235.
24 HUANG S J, ZHAO J W, LIU Z Y. Cost-effective training of deep cnns with active model adaptation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, UK:ACM, 2018: 1580-1588.
25 KARIMI R, FREUDENTHALER C, NANOPOULOS A, et al. Non-myopic active learning for recommender systems based on Matrix Factorization[C]//IEEE International Conference on Information Reuse and Integration. Las Vegas, USA: IEEE, 2011:299-303.
26 ZHOU D, WANG B, RAHIMI S M, et al. A study of recommending locations on location-based social network by collaborative filtering[C]//Canadian Conference on Artificial Intelligence. Toronto, Canada:Springer, 2012: 255-266.
27 RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L. Factorizing personalized markov chains for next-basket recommendation[C]//Proceedings of the 19th international conference on World wide web. North Carolina, USA:ACM, 2010: 811-820.
[1] Peng WAN. Object detection of 3D point clouds based on F-PointNet [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 98-104.
[2] Yutian LIU, Runjia SUN, Hongtao WANG, Xueping GU. Review on application of artificial intelligence in power system restoration [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 1-8.
[3] Zhixiang LIANG,Xiaoming LIU,Ying MU,Yutian LIU. Prediction method of wind power and PV ramp event based on deep learning [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 24-28.
[4] Ji ZHANG,Cui JIN,Hongyuan WANG,Shoubing CHEN. Pedestrian recognition based on singular value decomposition pedestrian alignment network [J]. Journal of Shandong University(Engineering Science), 2019, 49(5): 91-97.
[5] Chengbin ZHANG,Hui ZHAO,Zongyu CAO. The vulnerability mining method for KWP2000 protocol based on deep learning and fuzzing [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 17-22.
[6] Yun HU,Shu ZHANG,Hui LI,Kankan SHE,Jun SHI. Recommendation algorithm based on trust network reconfiguration [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 42-46.
[7] Xiaoxiong HOU,Xinzheng XU,Jiong ZHU,Yanyan GUO. Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 74-79.
[8] Lizhao LI,Guoyong CAI,Jiao PAN. A microblog rumor events detection method based on C-GRU [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 102-106, 115.
[9] XIE Zhifeng, WU Jiaping, MA Lizhuang. Chinese financial news classification method based on convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 34-39.
[10] TANG Leshuang, TIAN Guohui, HUANG Bin. An object fusion recognition algorithm based on DSmT [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 50-56.
[11] ZHOU Funa, GAO Yulin, WANG Jiayu, WEN Chenglin. Early diagnosis and life prognosis for slowlyvarying fault based on deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 30-37.
[12] HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47.
[13] HUANG Dan, WANG Zhihai, LIU Haiyang. A local collaborative filtering algorithm based on ranking recommendation tasks [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(5): 29-36.
[14] LIN Yaojin, ZHANG Jia, LIN Menglei, WANG Juan. A method of collaborative filtering recommendation based on fuzzy information entropy [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(5): 13-20.
[15] LI Shuo, SHI Yuliang. The method of spot cluster recommendation in location-based social networks [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(3): 44-50.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[2] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .
[3] LIU Wen-liang, ZHU Wei-hong, CHEN Di, ZHANG Hong-quan. Detection and tracking of moving targets using the morphology match in radar images[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 31 -36 .
[4] Yue Khing Toh1， XIAO Wendong2， XIE Lihua1. Wireless sensor network for distributed target tracking: practices via real test bed development[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 50 -56 .
[5] LIU Zhongguo,ZHANG Xiaojing,LIU Boqiang,LIU Changchun, . The development of ultrasonic characterization of the biological tissue elasticity[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(3): 34 -38 .
[6] YUE Yuan-Zheng. Relaxation in glasses far from equilibrium[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 1 -20 .
[7] LI Hui-ping, ZHAO Guo-qun, ZHANG Lei, HE Lian-fang. The development status of hot stamping and quenching of ultra high-strength steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 69 -74 .
[8] LIU Xin 1, SONG Sili 1, WANG Xinhong 2. [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 98 -100 .
[9] HU Tian-liang,LI Peng,ZHANG Cheng-rui,ZUO Yi . Design of a QEP decode counter based on VHDL[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(3): 10 -13 .
[10] XU Li-li,JI Zhong,XIA Ji-mei . The optimum algorithm for the container loading problem with homogeneous cargoes[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(3): 14 -17 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd