Please wait a minute...
Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (4): 39-48    DOI: 10.11925/infotech.2096-3467.2021.0683
Current Issue | Archive | Adv Search |
Measuring User Item Quality with Rating Analysis for Deep Recommendation Model
Zheng Xiao,Li Shuqing(),Zhang Zhiwang
College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
Download: PDF (769 KB)   HTML ( 34 ) 
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper designs a new deep learning algorithm to improve the recommendation results. [Methods] Our model evaluated user and item quality features from user ratings and item quality consistency, numerical distribution of ratings and time-period-based numerical distribution of ratings. [Results] We examined our model with the MovieLens dataset, and found the MAE and MSE were improved by up to 3.71% and 4.24%, respectively. [Limitations] More research is needed to explore a quality index evaluation method including attribute features of user and items. [Conclusions] The proposed model generates more accurate scoring prediction, and effectively improves the quality of recommendation.
Key words： Recommendation System      Deep Learning      User and Item Quality      Effective Features     
Received: 07 July 2021      Published: 12 May 2022
ZTFLH:  TP393  
Fund:Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China(19KJA510011)
Corresponding Authors: Li Shuqing,ORCID：0000-0001-9814-5766     E-mail: leeshuqing@163.com
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Xiao Zheng
Shuqing Li
Zhiwang Zhang

Cite this article:

Zheng Xiao, Li Shuqing, Zhang Zhiwang. Measuring User Item Quality with Rating Analysis for Deep Recommendation Model. Data Analysis and Knowledge Discovery, 2022, 6(4): 39-48.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0683     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I4/39

Deep Recommendation Model Based on User and Item Quality Features
评分数值 1 2 3 4 5
频率 3 1 2 4 1
Example of Low Ratings Count and Variant Ratings
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 2.90 1.21
钟形α=β=5 3.12 1.23
Result of Example of Low Ratings Count and Variant Ratings
评分数值 1 2 3 4 5
频率 0 0 3 7 1
Example of Low Ratings Count and Similar Ratings
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 1.89 1.46
钟形α=β=5 1.95 1.42
Result of Example of Low Ratings Count and Similar Ratings
评分数值 1 2 3 4 5
频率 125 75 25 75 50
Example of High Ratings Count
Beta分布策略 用户质量分数 MAE
U形α=β=0.5 2.86 1.02
钟形α=β=5 3.03 1.01
Result of High Ratings Count
案例

评分数值		 1 2 3 4 5
案例1 评分频率 5 1 1 1 1
Mean Weights 0.566 0.111 0.111 0.111 0.111
NDR Weights 0.578 0.147 0.123 0.191 0.060
RAUQ_Beta Weights 0.511 0.022 0.027 0.043 0.396
案例2 评分频率 125 25 25 25 25
Mean Weights 0.566 0.111 0.111 0.111 0.111
NDR Weights 0.573 0.140 0.122 0.096 0.069
RAUQ_Beta Weights 0.590 0.168 0.134 0.083 0.025
Example of Weights per Level Generated Using Mean, NDR and RAUQ_Beta
评分时间 1 2 3 4 5
评分数量 799 620 21 8 8
平均评分 4.176 4.140 4.095 4 4.375
分配权重 0.549 0.426 0.015 0.005 0.005
Result of Average Rating Time for Item 1
稠密时间段评分数量 207 439 145 426 160
平均评分 4.169 4.202 4.134 4.117 4.206
分配权重 0.290 0.415 0.105 0.155 0.035
Result of Dense Rating Time Division for Item 1
算法 MAE MSE
ConvMF 0.747 0.842
NDRU 0.740 0.839
EPIR 0.735 0.834
RAUIQ_CORR 0.728 0.825
Results of User Quality
算法 MAE MSE
Average 0.755 0.860
HITS 0.750 0.851
ConvMF 0.745 0.842
RAUIQ_CORR 0.728 0.825
Results of Item Quality
算法 ML-100k ML-1M
Mean 0.805 0.776
WS 0.799 0.768
Correlation 0.781 0.755
LRMF 0.772 0.741
ConvMF 0.778 0.746
CDL 0.766 0.737
RAUIQ_CORR 0.761 0.728
RAUQ_Beta 0.757 0.725
Results of Each User Quality Algorithm
算法 ML-100k ML-1M
Average 0.790 0.755
HITS 0.781 0.750
ConvMF 0.778 0.746
RAUIQ_CORR 0.766 0.728
RAIQ_TIME 0.758 0.724
3 Results of Each Item Quality Algorithm
[1] Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for Collaborative Filtering[C]//Proceedings of the 24th International Conference on Machine Learning. 2007: 791-798.
[2] 刘凯, 张立民, 周立军. 深度学习在信息推荐系统的应用综述[J]. 小型微型计算机系统, 2019, 40(4):738-743.
[2] ( Liu Kai, Zhang Limin, Zhou Lijun. Survey of Deep Learning Applied in Information Recommendation System[J]. Journal of Chinese Computer Systems, 2019, 40(4):738-743.)
[3] Lian J X, Zhou X H, Zhang F Z, et al. XDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 1754-1763.
[4] Kim D, Park C, Oh J, et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation[C]//Proceedings of the 10th ACM Conference on Recommender Systems. 2016: 233-240.
[5] Filho R J R, Wehrmann J, Barros R C. Leveraging Deep Visual Features for Content-Based Movie Recommender Systems[C]//Proceedings of the 2017 International Joint Conference on Neural Networks. IEEE, 2017: 604-611.
[6] Messina P, Dominguez V, Parra D, et al. Content-Based Artwork Recommendation: Integrating Painting Metadata with Neural and Manually-Engineered Visual Features[J]. User Modeling and User-Adapted Interaction, 2019, 29(2):251-290.
doi: 10.1007/s11257-018-9206-9
[7] Wang H, Wang N Y, Yeung D Y. Collaborative Deep Learning for Recommender Systems[C]//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1235-1244.
[8] 邓存彬, 虞慧群, 范贵生. 融合动态协同过滤和深度学习的推荐算法[J]. 计算机科学, 2019, 46(8):28-34.
[8] ( Deng Cunbin, Yu Huiqun, Fan Guisheng. Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation[J]. Computer Science, 2019, 46(8):28-34.)
[9] 胡朝举, 郑浩. 融合上下文信息的深度推荐模型[J]. 计算机应用研究, 2021, 38(4):1074-1078.
[9] ( Hu Chaoju, Zheng Hao. Deep Recommendation Model with Context Information[J]. Application Research of Computers, 2021, 38(4):1074-1078.)
[10] 李涛, 符丁. 基于协同过滤算法的自动化隐式评分音乐双重推荐系统[J]. 计算机测量与控制, 2018, 26(11):171-175.
[10] ( Li Tao, Fu Ding. An Automated Implicit Scoring Music Dual Recommendation System Based on Collaborative Filtering Algorithm[J]. Computer Measurement & Control, 2018, 26(11):171-175.)
[11] Qian F L, Zhao S, Tang J, et al. SoRS: Social Recommendation Using Global Rating Reputation and Local Rating Similarity[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 461:61-72.
doi: 10.1016/j.physa.2016.05.025
[12] Abdel-Hafez A, Xu Y, Tian N. Item Reputation-Aware Recommender Systems[C]//Proceedings of the 16th International Conference on Information Integration and Web-Based Applications & Services. ACM, 2014: 79-86.
[13] Kim Y. Convolutional Neural Networks for Sentence Classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2014: 1746-1751.
[14] Zhou Y B, Lei T, Zhou T. A Robust Ranking Algorithm to Spamming[J]. Europhysics Letters, 2011, 94(4):48002.
doi: 10.1209/0295-5075/94/48002
[15] Radde S, Freitag B. Using Bayesian Networks to Infer Product Rankings from User Needs[C]//Proceedings of the UMAP 2010 Workshop on Intelligent Techniques for Web Personalization and Recommender Systems. 2010.
[16] Kleinberg J M. Authoritative Sources in a Hyperlinked Environment[J]. Journal of the ACM, 1999, 46(5):604-632.
doi: 10.1145/324133.324140
[17] 王岩, 张杰, 许合利. 结合用户兴趣和改进的协同过滤推荐算法[J]. 小型微型计算机系统, 2020, 41(8):1665-1669.
[17] ( Wang Yan, Zhang Jie, Xu Heli. Combining User Interests with Improved Collaborative Filtering Recommendation Algorithm[J]. Journal of Chinese Computer Systems, 2020, 41(8):1665-1669.)
[18] Abdel-Hafez A J. Reputation Model Based on Rating Data and Application in Recommender Systems[D]. Brisbane: Queensland University of Technology, 2016.
[19] 于鹏华. 数据数量与质量敏感的推荐系统若干问题研究[D]. 杭州: 浙江大学, 2016.
[19] ( Yu Penghua. Research on Data Quantity and Quality Sensitive Recommender Systems[D]. Hangzhou: Zhejiang University, 2016.)
[20] Abdel-Hafez A, Xu Y, Jøsang A. A Rating Aggregation Method for Generating Product Reputations[C]//Proceedings of the 25th ACM Conference on Hypertext and Social Media. 2014: 291-293.
[21] McNally K, O’Mahony M P, Smyth B, et al. Towards a Reputation-Based Model of Social Web Search[C]// Proceedings of the 15th International Conference on Intelligent User Interfaces. 2010: 179-188.
[22] Li S, Wen J H, Luo F J, et al. A Location and Reputation Aware Matrix Factorization Approach for Personalized Quality of Service Prediction[C]//Proceedings of the 2017 IEEE International Conference on Web Services. IEEE, 2017: 652-659.
[1] Wang Lu, Le Xiaoqiu. Research Progress on Citation Analysis of Scientific Papers[J]. 数据分析与知识发现, 2022, 6(4): 1-15.
[2] Yu Chuanming, Lin Hongjun, Zhang Zhengang. Joint Extraction Model for Entities and Events with Multi-task Deep Learning[J]. 数据分析与知识发现, 2022, 6(2/3): 117-128.
[3] Zhang Yunqiu, Li Bocheng, Chen Yan. Automatic Classification with Unbalanced Data for Electronic Medical Records[J]. 数据分析与知识发现, 2022, 6(2/3): 233-241.
[4] Zhang Fangcong, Qin Qiuli, Jiang Yong, Zhuang Runtao. Named Entity Recognition for Chinese EMR with RoBERTa-WWM-BiLSTM-CRF[J]. 数据分析与知识发现, 2022, 6(2/3): 251-262.
[5] Hu Yamin, Wu Xiaoyan, Chen Fang. Review of Technology Term Recognition Studies Based on Machine Learning[J]. 数据分析与知识发现, 2022, 6(2/3): 7-17.
[6] Zhou Zeyu,Wang Hao,Zhao Zibo,Li Yueyan,Zhang Xiaoqin. Construction and Application of GCN Model for Text Classification with Associated Information[J]. 数据分析与知识发现, 2021, 5(9): 31-41.
[7] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[8] Zhao Danning,Mu Dongmei,Bai Sen. Automatically Extracting Structural Elements of Sci-Tech Literature Abstracts Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(7): 70-80.
[9] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[10] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[11] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[12] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[13] Feng Yong,Liu Yang,Xu Hongyan,Wang Rongbing,Zhang Yonggang. Recommendation Model Incorporating Neighbor Reviews for GRU Products[J]. 数据分析与知识发现, 2021, 5(3): 78-87.
[14] Cheng Bin,Shi Shuicai,Du Yuncheng,Xiao Shibin. Keyword Extraction for Journals Based on Part-of-Speech and BiLSTM-CRF Combined Model[J]. 数据分析与知识发现, 2021, 5(3): 101-108.
[15] Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn