Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (8): 62-67    DOI: 10.11925/infotech.2096-3467.2018.1000
Current Issue | Archive | Adv Search |
Collaborative Filtering Recommendation Based on Item Quality and User Ratings
Fusen Jiao,Shuqing Li()
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210046, China
Download: PDF (512 KB)   HTML ( 14
Export: BibTeX | EndNote (RIS)      

[Objective] This paper proposes a modified collaborative filtering algorithm, aiming to improve the results of personalized recommendations. [Methods] First, we evaluated item quality and corrected user ratings based on their previous records. Then, we identified users with similar interests to generate better recommendations. [Results] We tested the new algorithm on MovieLens dataset and found the MAE was 4.7% higher than those of the traditional or other modified methods. [Limitations] The new algorithm does not address the interests drifting issues. [Conclusions] The proposed algorithm could recommend products to consumers more effectively.

Key wordsRecommender System      Collaborative Filtering      Item Quality      User Rating Correction     
Received: 25 August 2018      Published: 29 September 2019
ZTFLH:  TP391 G35  
Corresponding Authors: Shuqing Li     E-mail:

Cite this article:

Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings. Data Analysis and Knowledge Discovery, 2019, 3(8): 62-67.

URL:     OR

Average HITS Bayes IMDB
Planet Earth Forrest Gump The Shawshank Redemption The Shawshank Redemption
The Shawshank Redemption Pulp Fiction The Godfather The Godfather
The Usual Suspects The Matrix The Usual Suspects The Godfather: Part II
Schindler’s List The Shawshank Redemption The Godfather: Part II The Dark Knight
The Godfather: Part II The Silence of the Lambs Schindler’s List 12 Angry Men
12 Angry Men Jurassic Park Seven Samurai Schindler’s List
Fight Club Star Wars: Episode IV Fight Club The Lord of the Rings: The Return of the King
Pulp Fiction Star Wars: Episode V 12 Angry Men Pulp Fiction
Planet Earth Terminator 2: Judgment Day Spirited Away Il buono, il brutto, il cattivo
Human Planet Braveheart Pulp Fiction Fight Club
[1] 许海玲, 吴潇, 李晓东 , 等. 互联网推荐系统比较研究[J]. 软件学报, 2009,20(2):350-362.
[1] ( Xu Hailing, Wu Xiao, Li Xiaodong , et al. Comparison Study of Internet Recommendation System[J]. Journal of Software, 2009,20(2):350-362.)
[2] 邓爱林, 朱扬勇, 施伯乐 . 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003,14(9):1621-1628.
[2] ( Deng Ailin, Zhu Yangyong, Shi Bole . A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction[J]. Journal of Software, 2003,14(9):1621-1628.)
[3] Linden G, Smith B, York J . Recommendations: Item-to-Item Collaborative Filtering[J]. IEEE Internet Computing, 2003,7(1):76-80.
[4] 马宏伟, 张光卫, 李鹏 . 协同过滤推荐算法综述[J]. 小型微型计算机系统, 2009,30(7):1282-1288.
[4] ( Ma Hongwei, Zhang Guangwei, Li Peng . Survey of Collaborative Filtering Algorithms[J]. Journal of Chinese Computer Systems, 2009,30(7):1282-1288.)
[5] Kaleli C . An Entropy-Based Neighbor Selection Approach for Collaborative Filtering[J]. Knowledge-Based Systems, 2014,56:273-280.
[6] Cosley D, Lam S K, Albert I, et al. Is Seeing Believing?: How Recommender System Interfaces Affect Users’ Opinions [C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2003: 585-592.
[7] 韩亚楠, 曹菡, 刘亮亮 . 基于评分矩阵填充与用户兴趣的协同过滤推荐算法[J]. 计算机工程, 2016,42(1):36-40.
doi: 10.3969/j.issn.1000-3428.2016.01.007
[7] ( Han Ya’nan, Cao Han, Liu Liangliang . Collaborative Filtering Recommendation Algorithm Based on Score Matrix Filling and User Interest[J]. Computer Engineering, 2016,42(1):36-40.)
doi: 10.3969/j.issn.1000-3428.2016.01.007
[8] 彭石, 周志彬, 王国军 . 基于评分矩阵预填充的协同过滤算法[J]. 计算机工程, 2013,39(1):175-178.
doi: 10.3969/j.issn.1000-3428.2013.01.037
[8] ( Peng Shi, Zhou Zhibin, Wang Guojun . Collaborative Filtering Algorithm Based on Rating Matrix Pre-filling[J]. Computer Engineering, 2013,39(1):175-178.)
doi: 10.3969/j.issn.1000-3428.2013.01.037
[9] 贾伟洋, 李书琴, 李昕宇 , 等. 基于离散量和用户兴趣贴近度的协同过滤推荐算法[J]. 计算机工程, 2018,44(1):226-232.
[9] ( Jia Weiyang, Li Shuqin, Li Xinyu , et al. Collaborative Filtering Recommendation Algorithm Based on Discrete Quantity and User Interests Approach Degree[J]. Computer Engineering, 2018,44(1):226-232.)
[10] 张佳, 林耀进, 林梦雷 , 等. 基于目标用户近邻修正的协同过滤算法[J]. 模式识别与人工智能, 2015,28(9):802-810.
doi: 10.16451/j.cnki.issn1003-6059.201509005
[10] ( Zhang Jia, Lin Yaojin, Lin Menglei , et al. Target User’s Neighbors Modification Based Collaborative Filtering[J]. Pattern Recognition and Artificial Intelligence, 2015,28(9):802-810.)
doi: 10.16451/j.cnki.issn1003-6059.201509005
[11] Anand D, Bharadwaj K K . Utilizing Various Sparsity Measures for Enhancing Accuracy of Collaborative Recommender Systems Based on Local and Global Similarities[J]. Expert Systems with Applications, 2011,38(5):5101-5109.
[12] Kim T H, Yang S B. An Effective Threshold-Based Neighbor Selection in Collaborative Filtering [C]// Proceedings of the 29th European Conference on IR Research. Springer, 2007: 712-715.
[13] 贾冬艳, 张付志 . 基于双重邻居选取策略的协同过滤推荐算法[J]. 计算机研究与发展, 2013,50(5):1076-1084.
[13] ( Jia Dongyan, Zhang Fuzhi . A Collaborative Filtering Recommendation Algorithm Based on Double Neighbor Choosing Strategy[J]. Journal of Computer Research and Development, 2013,50(5):1076-1084.)
[14] 于阳, 于洪涛, 黄瑞阳 . 基于熵优化近邻选择的协同过滤推荐算法[J]. 计算机应用研究, 2017,34(9):2618-2623.
[14] ( Yu Yang, Yu Hongtao, Huang Ruiyang . Collaborative Filtering Recommendation Algorithm Based on Entropy Optimization Nearest-neighbor Selection[J]. Application Research of Computers, 2017,34(9):2618-2623.)
[15] Guan Y, Cai S, Shang M . Recommendation Algorithm Based on Item Quality and User Rating Preferences[J]. Frontiers of Computer Science, 2014,8(2):289-297.
[16] Page L, Brin S, Motwani R , et al. The PageRank Citation Ranking: Bringing Order to the Web[R]. Stanford InfoLab, 1999.
[17] 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.
[18] 侯银秀, 李伟卿, 王伟军 , 等. 基于用户偏好与商品属性情感匹配的图书个性化推荐研究[J]. 数据分析与知识发现, 2017,1(8):9-17.
[18] ( Hou Yinxiu, Li Weiqing, Wang Weijun , et al. Personalized Book Recommendation Based on User Preferences and Commodity Features[J]. Data Analysis and Knowledge Discovery, 2017,1(8):9-17.)
[19] Kleinberg J M . Authoritative Sources in a Hyperlinked Environment[J]. Journal of the ACM, 1999,46(5):604-632.
[20] IMDB. Ratings and Reviews for New Movies and TV Shows - IMDb[EB/OL].[2019-08-01]. .
[1] Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[2] Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[3] Yu Shuo,Hayat Dino Bedru,Chu Xinbei,Yuan Yuyuan,Wan Liangtian,Xia Feng. Understanding Serendipity in Science: A Survey[J]. 数据分析与知识发现, 2021, 5(1): 16-35.
[4] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[5] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[6] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[7] Ding Yong,Chen Xi,Jiang Cuiqing,Wang Zhao. Predicting Online Ratings with Network Representation Learning and XGBoost[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
[8] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[9] 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.
[10] Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[11] Wang Yong,Wang Yongdong,Guo Huifang,Zhou Yumin. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[12] Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[13] Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[14] Qin Xingxin,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[15] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938