[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.
焦富森,李树青. 基于物品质量和用户评分修正的协同过滤推荐算法 *[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings. Data Analysis and Knowledge Discovery, 2019, 3(8): 62-67.
( Ma Hongwei, Zhang Guangwei, Li Peng . Survey of Collaborative Filtering Algorithms[J]. Journal of Chinese Computer Systems, 2009,30(7):1282-1288.)
Kaleli C . An Entropy-Based Neighbor Selection Approach for Collaborative Filtering[J]. Knowledge-Based Systems, 2014,56:273-280.
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.
( 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.)
( 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.)
( 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.)
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.
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.
( 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.)
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.
Page L, Brin S, Motwani R , et al. The PageRank Citation Ranking: Bringing Order to the Web[R]. Stanford InfoLab, 1999.
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.