Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 45-58    DOI: 10.11925/infotech.2096-3467.2021.0292
Current Issue | Archive | Adv Search |
A Personalized Recommendation Model with Time Series Fluctuation of User Interest
Ding Hao1,2,Ai Wenhua1,2,Hu Guangwei1,2(),Li Shuqing3,Suo Wei4
1School of Information Management, Nanjing University, Nanjing 210023, China
2Institute of Government Data Resources,Nanjing University,Nanjing 210023, China
3School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
4School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191,China
Download: PDF (3135 KB)   HTML ( 19
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper constructs a prediction model based on hybrid time series to improve the recommendation accuracy. [Methods] First, we constructed a trend prediction model using neural network and fuzzy clustering technique for interest fluctuations at different magnitudes. Then, we utilized neural network to extract and predict the sliding features of small fluctuation series. Finally, we used the membership degree of fuzzy clustering to divide the relationship for large fluctuation series data. [Results] User simulation tests with four groups of experimental data showed that extracted data features for different amplitudes of interest fluctuation yielded more accurate prediction results, which were 19.18% lower than other algorithms’ RMSE and 45.78% higher than other algorithms' Hit-Ratio. [Limitations] The analysis of time fluctuation relies on historical data, therefore, additional cold-start algorithm is needed to preprocess the sparse historical data. [Conclusions] This method could effectively process the fluctuation of interest, and improve the personalized information services.

Key wordsTime Series      Interest Fluctuation      Neural Networks      Fuzzy Clustering      Hybrid Forecasting      Personalized Recommendation     
Received: 24 March 2021      Published: 23 December 2021
ZTFLH:  TP391  
Fund:National Social Science Fund of China(20&ZD154);Innovation and Entrepreneurship Big Data and Theoretical Research Project of Nanjing University in 2021(NJU-DI2021004);Key University Science Research Project of Jiangsu Province(19KJA510011)
Corresponding Authors: Hu Guangwei,ORCID:0000-0003-1303-363X     E-mail: hugw@nju.edu.cn

Cite this article:

Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest. Data Analysis and Knowledge Discovery, 2021, 5(11): 45-58.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0292     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/45

The Framework of Model
Structure Division of Project Compound Category
Schematic Diagram of Neural Network Model
Schematic Diagram of Time Series Fuzzy Clustering Model
评价标准划分(K=5)
子区间 [0,1] (1,2) [2,3] (3,4) [4,5]
模糊集 A1 A2 A3 A4 A5
评价标准 评价很低 评价较低 评价中等 评价较高 评价很高
Division of Evaluation Criteria for Fuzzy Time Series
项目ID 标题 类型
24 Powder (1995) Drama|Sci-Fi
25 Leaving Las Vegas (1995) Drama|Romance
26 Othello (1995) Drama
30 Now and Then (1995) Children|Drama
Basic Information of MovieLens Dataset
数据集划分 用户 项目 评分 稀疏度
ML10M@60% 42 941 6 089 10 000 054 98.69%
ML10M@80% 57 254 8 273 8 195 257 98.27%
ML20M@60% 83 096 16 625 10 025 571 99.27%
ML20M@80% 110 795 20 213 17 425 571 99.22%
Test Data Set Size
Percentage of Each Tag Data in MovieLens Dataset
Parameters α and δ Performance Verification Results in Different Test
时间单位 兴趣波动幅度 α-取值区间比例
α-区间 [0,0.05) [0.05,0.10) [0.10,0.15) [0.15,0.20) [0.20,0.25)
0.480 3 0.312 5 0.059 1 0.036 7 0.031 6
0.468 7 0.321 7 0.066 4 0.038 8 0.026 6
0.3991 0.340 4 0.062 2 0.053 3 0.036 8
α-区间 [0.25,0.30) [0.30,0.35) [0.35,0.40) [0.40,0.45) [0.45,∞)
0.024 4 0.021 3 0.019 4 0.011 2 0.003 5
0.022 5 0.020 9 0.017 4 0.011 8 0.005 2
0.032 4 0.027 3 0.024 7 0.017 1 0.006 7
Overall Distribution of User Interest Fluctuation Range
Time Series Variance Change of User Interest Amplitude Fluctuation
The Relationship Between Hit Ratio and VAR
项目类型 用户
用户A 用户B 用户N
Adventure A2 → A2,A3
A3 → A3,A4,A5
A1 → A2
A2 → A2,A3,A4
A2 → A2,A3
A3 → A2,A3,A4
Animation A2 → A2 A2 → A5
[Action,Adventure] A2 → A3
A3 → A3,A4
A2 → A3 A3 → A2,A3,A4
[Action,Comedy] A2 → A3
A3 → A1,A2,A3
A2 → A3 A2 → A3
A3 → A4
[Action,War,Western] A2 → A2,A3
A3 → A3,A4
A2 → A3
A3 → A3
A2 → A3
A3 → A2,A3
Fuzzy Relation Group of User Fuzzy Time Series (Some Examples)
预测方法 MAE RSME
随机预测 1.876 3.879
仅用神经网络拟合预测 0.843 1.338
仅用模糊时间序列预测 0.952 1.521
本文HTSRF方法 0.681 0.860
Evaluation of Different Interest Fluctuation Prediction Methods
Iterative Training Prediction Performance of Different Models
方法名称 平均迭代时间/s 平均响应时间/s 平均内存占用/MB
HTSRF 13 0.28 60
PFM 17 0.79 73
SVD++ 592.3 7.86 200
DREAM 30.8 0.25 105
RPF 19 1.27 89
Time and Space Consumption of Different Time Series Models
[1] 冯永, 张备, 强保华, 等. MN-HDRM: 长短兴趣多神经网络混合动态推荐模型[J]. 计算机学报, 2019, 42(1):16-28.
[1] (Feng Yong, Zhang Bei, Qiang Baohua, et al. MN-HDRM: Hybrid Dynamic Recommendation Model Based on Long Short Interest and Multi Neural Networks[J]. Chinese Journal of Computers, 2019, 42(1):16-28.)
[2] 刘占兵, 肖诗斌. 基于用户兴趣模糊聚类的协同过滤算法[J]. 现代图书情报技术, 2015(11):12-17.
[2] (Liu Zhanbing, Xiao Shibin. Collaborative Filtering Algorithm Based on User Interest Fuzzy Clustering[J]. New Technology of Library and Information Service, 2015(11):12-17.)
[3] 毕强, 刘健. 基于领域本体的数字文献资源聚合及服务推荐方法研究[J]. 情报学报, 2017, 36(5):452-460.
[3] (Bi Qiang, Liu Jian. Research on Digital Literature Resource Aggregation and Service Recommendation Method Based on Domain Ontology[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(5):452-460.)
[4] 毕强, 刘健. 数字文献资源内容服务推荐方法研究[J]. 现代图书情报技术, 2015(12):21-27, 105.
[4] (Bi Qiang, Liu Jian. Research on Content Service Recommendation Method of Digital Literature Resources[J]. New Technology of Library and Information Service, 2015(12):21-27, 105.)
[5] 蒲菁川. 微博用户兴趣建模及其动态性研究[D]. 哈尔滨:哈尔滨工业大学, 2014.
[5] (Pu Jingchuan. Modeling and Dynamics of Microblog User Interest[D]. Harbin: Harbin Institute of Technology, 2014.)
[6] 曾金, 陆伟, 丁恒, 等. 基于图像语义的用户兴趣建模[J]. 数据分析与知识发现, 2017, 1(4):76-83.
[6] (Zeng Jin, Lu Wei, Ding Heng, et al. User Interest Modeling Based on Image Semantics[J]. Data Analysis and Knowledge Discovery, 2017, 1(4):76-83.)
[7] Deng Z H, Huang L, Wang C D, et al. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2019: 61-68.
[8] 印鉴, 王智圣, 李琪, 等. 基于大规模隐式反馈的个性化推荐[J]. 软件学报, 2014, 25(9):1953-1966.
[8] (Yin Jian, Wang Zhisheng, Li Qi, et al. Personalized Recommendation Based on Large-scale Implicit Feedback[J]. Journal of Software, 2014, 25(9):1953-1966.)
[9] 易明, 操玉杰, 沈劲枝, 等. 社会化标签系统中基于密度聚类的Web用户兴趣建模方法[J]. 情报学报, 2011, 30(1):37-43.
[9] (Yi Ming, Cao Yujie, Shen Jinzhi, et al. Web User Interest Modeling Method Based on Density Clustering in Social Tagging System[J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(1):37-43.)
[10] 颜端武, 刘明岩, 许应楠. 基于领域本体的细粒度用户兴趣建模研究[J]. 情报学报, 2010, 29(3):433-442.
[10] (Yan Duanwu, Liu Mingyan, Xu Yingnan. Research on Fine Grained User Interest Modeling Based on Domain Ontology[J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(3):433-442.)
[11] 孙雨生, 刘伟, 仇蓉蓉, 等. 国内用户兴趣建模研究进展[J]. 情报杂志, 2013, 32(5):145-149, 165.
[11] (Sun Yusheng, Liu Wei, Qiu Rongrong, et al. Research Progress of User Interest Modeling in China[J]. Journal of Intelligence, 2013, 32(5):145-149, 165.)
[12] 石宇, 胡昌平, 时颖惠. 个性化推荐中基于认知的用户兴趣建模研究[J]. 情报科学, 2019, 37(6):37-41.
[12] (Shi Yu, Hu Changping, Shi Yinghui. Research on User Interest Modeling Based on Cognition in Personalized Recommendation[J]. Information Science, 2019, 37(6):37-41.)
[13] Jiang B, Sha Y. Modeling Temporal Dynamics of User Interests in Online Social Networks[J]. Procedia Computer Science, 2015, 51(1):503-512.
doi: 10.1016/j.procs.2015.05.275
[14] 刘淇. 基于用户兴趣建模的推荐方法及应用研究[D]. 合肥: 中国科学技术大学, 2013.
[14] (Liu Qi. Research on Recommendation Method and Application Based on User Interest Modeling[D]. Hefei: University of Science and Technology of China, 2013.)
[15] 李媛媛, 李旭晖. 结合本体与社会化标签的用户动态兴趣建模研究[J]. 情报学报, 2020, 39(4):436-449.
[15] (Li Yuanyuan, Li Xuhui. Research on User Dynamic Interest Modeling Based on Ontology and Social Tag[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(4):436-449.)
[16] 张彬, 徐建民, 吴树芳. 基于多源用户标签的跨域兴趣融合模型研究[J]. 情报科学, 2020, 38(4):147-152, 162.
[16] (Zhang Bin, Xu Jianmin, Wu Shufang. Research on Cross Domain Interest Fusion Model Based on Multi-Source User Tags[J]. Information Science, 2020, 38(4):147-152, 162.)
[17] Moews B, Herrmann J M, Ibikunle G. Lagged Correlation-based Deep Learning for Directional Trend Change Prediction in Financial Time Series[J]. Expert Systems with Applications, 2019, 120:197-206.
doi: 10.1016/j.eswa.2018.11.027
[18] Patel J, Shah S, Thakkar P, et al. Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques[J]. Expert Systems with Applications, 2015, 42(1):259-268.
doi: 10.1016/j.eswa.2014.07.040
[19] Yang H F, Chen Y P P. Hybrid Deep Learning and Empirical Mode Decomposition Model for Time Series Applications[J]. Expert Systems with Applications, 2019, 120:128-138.
doi: 10.1016/j.eswa.2018.11.019
[20] 张大斌, 李红燕, 刘肖, 等. 非线性时间序列的小波-模糊神经网络集成预测方法[J]. 中国管理科学, 2013, 21(S2):647-651.
[20] (Zhang Dabin, Li Hongyan, Liu Xiao, et al. Wavelet Fuzzy Neural Network Ensemble Prediction Method for Nonlinear Time Series[J]. Chinese Journal of Management Science, 2013, 21(S2):647-651.)
[21] Choi S M, Ko S K, Han Y S. A Movie Recommendation Algorithm Based on Genre Correlations[J]. Expert Systems with Applications, 2012, 39(9):8079-8085.
doi: 10.1016/j.eswa.2012.01.132
[22] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 24-26.
[22] (Zhou Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 24-26.)
[23] Vlachos M, Duenner C, Heckel R, et al. Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems[J]. IEEE Transactions on Knowledge & Data Engineering, 2019, 31(7):1253-1266.
[1] Wu Yanwen, Cai Qiuting, Liu Zhi, Deng Yunze. Digital Resource Recommendation Based on Multi-Source Data and Scene Similarity Calculation[J]. 数据分析与知识发现, 2021, 5(11): 114-123.
[2] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[3] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[4] Feng Wengang,Jiang Zhaofeifan. Improving Security Checks and Passenger Risk Evaluation with Classification of Airline Passengers[J]. 数据分析与知识发现, 2020, 4(12): 105-119.
[5] Yan Jinghua,Hou Miaomiao. Predicting Time Series of Theft Crimes Based on LSTM Network[J]. 数据分析与知识发现, 2020, 4(11): 84-91.
[6] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[7] Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags[J]. 数据分析与知识发现, 2019, 3(2): 21-32.
[8] Hao Ding,Shuqing Li. Personalized Recommendation Based on Predictive Analysis of User’s Interests[J]. 数据分析与知识发现, 2019, 3(11): 43-51.
[9] Xu Yuemei,Lv Sining,Cai Lianqiao,Zhang Xiaoya. Analyzing News Topic Evolution with Convolutional Neural Networks and Topic2Vec[J]. 数据分析与知识发现, 2018, 2(9): 31-41.
[10] Li Jie,Yang Fang,Xu Chenxi. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[11] Xiao Yanhui,Wang Xin,Feng Wen’gang,Tian Huawei,Wu Shaozhong,Li Lihua. Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks[J]. 数据分析与知识发现, 2018, 2(10): 15-20.
[12] Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
[13] Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[14] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[15] Xie Qi,Cui Mengtian. Group Similarity Based Hybrid Web Service Recommendation Algorithm[J]. 现代图书情报技术, 2016, 32(6): 80-87.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn