基于模糊偏好向量和物品质量的推荐算法
Recommendation Algorithm Based on Fuzzy Preference Vector and Item Quality
DOI: 10.12677/orf.2024.142128, PDF,    国家自然科学基金支持
作者: 艾 均, 孙 浩, 苏 湛, 周 越：上海理工大学光电信息与计算机工程学院，上海
关键词: 推荐系统；用户模糊偏好；物品质量排序；相似性网络；误差修正；Recommendation Systems； User Fuzzy Preference； Item Quality Ranking； Similarity Network； Error Correction
摘要: 推荐系统能够快速有效地帮助人们在海量信息中进行筛选和过滤，为用户智能化地推荐其感兴趣的物品，因此具有重要的理论意义和广泛的应用价值。针对现有研究中推荐误差和推荐列表排序可以进一步优化的需求。本文应用模糊隶属度函数以计算了多维物品品类向量上的用户偏好，并基于用户偏好特征和物品品类向量计算用户间相似性，然后，进一步基于相似性和用户相关性筛选构建用户相似性网络，利用用户相似性网络中的社区信息，对预测过程进行加权。同时，利用统计方法过滤物品的异常评分以度量物品质量排序，利用物品质量排序的优化结果辅助预测的误差修正。基于MovieLens的小型数据集进行了对比试验，与时下流行的几个算法进行对比，本文设计的算法有效地提高了预测准确性及推荐排序准确性，实验结果表明了利用用户模糊偏好、物品质量排序、相似性网络及误差修正手段，可以进一步挖掘推荐系统地潜在信息，在仅增加少量计算量的情况下，进一步提升了系统性能，并且保持了系统的可解释性。
Abstract: Recommendation systems can quickly and effectively help people filter and sift through massive amounts of information, intelligently recommending items of interest to users. Therefore, they have significant theoretical importance and wide application value. This paper addresses the need for further optimization of recommendation errors and recommendation list ranking in existing research. The fuzzy membership function is applied to calculate user preferences on multi-dimensional item category vectors. Based on user preference features and item category vectors, user similarity is calculated. Then, further based on similarity and user relevance, a user similarity network is constructed by filtering and selection. The community information in the user similarity network is used to weight the prediction process. At the same time, statistical methods are used to filter out abnormal ratings of items to measure item quality ranking, and the optimized results of item quality ranking are used to assist in the correction of prediction errors. A comparative experiment was conducted on a small dataset from MovieLens, and the algorithm designed in this paper was compared with several popular algorithms. The results show that the designed algorithm effectively improves prediction accuracy and recommendation ranking accuracy. The experimental results demonstrate that by using user fuzzy preferences, item quality ranking, similarity network, and error correction methods, the potential information of the recommendation system can be further mined. With only a small amount of additional computation, system performance is further improved, and the system’s interpretability is maintained.
文章引用：艾均, 孙浩, 苏湛, 周越. 基于模糊偏好向量和物品质量的推荐算法[J]. 运筹与模糊学, 2024, 14(2): 218-233. https://doi.org/10.12677/orf.2024.142128

参考文献

[1] Aggarwal, C.C. (2016) An Introduction to Recommender Systems. In: Aggarwal, C.C., Ed., Recommender Systems, Springer, Cham, 1-28. [Google Scholar] [CrossRef] 
[2] Roy, D. and Dutta, M. (2022) A Systematic Review and Research Perspective on Recommender Systems. Journal of Big Data, 9, Article No. 59. [Google Scholar] [CrossRef] 
[3] Lekakos, G. and Caravelas, P. (2008) A Hybrid Approach for Movie Recommendation. Multimedia Tools and Applications, 36, 55-70. [Google Scholar] [CrossRef] 
[4] Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M. and Jararweh, Y. (2018) A New Collaborative Filtering Recommendation Algorithm Based on Dimensionality Reduction and Clustering Techniques. 2018 9th International Conference on Information and Communication Systems (ICICS), Irbid, 3-5 April 2018, 102-106. [Google Scholar] [CrossRef] 
[5] Gazdar, A. and Hidri, L. (2020) A New Similarity Measure for Collaborative Filtering Based Recommender Systems. Knowledge-Based Systems, 188, Article ID: 105058. [Google Scholar] [CrossRef] 
[6] Shojaei, M. and Saneifar, H. (2021) MFSR: A Novel Multi-Level Fuzzy Similarity Measure for Recommender Systems. Expert Systems with Applications, 177, Article ID: 114969. [Google Scholar] [CrossRef] 
[7] Tan, Z. and He, L. (2017) An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle. IEEE Access, 5, 27211-27228. [Google Scholar] [CrossRef] 
[8] Schwarz, M., Lobur, M. and Stekh, Y. (2017) Analysis of the Effectiveness of Similarity Measures for Recommender Systems. 2017 14th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, 21-25 February 2017, 275-277. [Google Scholar] [CrossRef] 
[9] Li, B. and Han, L. (2013) Distance Weighted Cosine Similarity Measure for Text Classification. In: Yin, H., et al., Eds., Intelligent Data Engineering and Automated Learning—IDEAL 2013, Springer, Berlin, 611-618. [Google Scholar] [CrossRef] 
[10] Davoudi, A. and Chatterjee, M. (2018) Social Trust Model for Rating Prediction in Recommender Systems: Effects of Similarity, Centrality, and Social Ties. Online Social Networks and Media, 7, 1-11. [Google Scholar] [CrossRef] 
[11] Zhang, F., Zhou, W., Sun, L., et al. (2017) Improvement of Pearson Similarity Coefficient Based on Item Frequency. 2017 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), Ningbo, 9-12 July 2017, 248-253. [Google Scholar] [CrossRef] 
[12] Yadav, D.K. and Katarya, R. (2018) Study on Recommender System Using Fuzzy Logic. 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, 15-16 February 2018, 50-54. [Google Scholar] [CrossRef] 
[13] Wang, W., Lu, J. and Zhang, G. (2014) A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems. In: Wen, Z. and Li, T., Eds., Foundations of Intelligent Systems, Springer, Berlin, 443-452. [Google Scholar] [CrossRef] 
[14] Kant, S., Mahara, T., Jain, V.K. and Jain, D.K. (2019) Fuzzy Logic Based Similarity Measure for Multimedia Contents Recommendation. Multimedia Tools and Applications, 78, 4107-4130. [Google Scholar] [CrossRef] 
[15] 白梅, 苌仕涵, 王习特. 基于位置的路网Skyline查询处理研究[J]. 计算机工程, 2022, 48(1): 127-134.
[16] 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.
[17] Li, N. and Li, C. (2009) Accumulative Influence Weight Collaborative Filtering Recommendation Approach. In: Chien, B.C. and Hong, T.P., Eds., Opportunities and Challenges for Next-Generation Applied Intelligence, Springer, Berlin, 73-78. [Google Scholar] [CrossRef] 
[18] Yera, R. and Martinez, L. (2017) Fuzzy Tools in Recommender Systems: A Survey. International Journal of Computational Intelligence Systems, 10, 776-803. [Google Scholar] [CrossRef] 
[19] Zhang, Z., Lin, H., Liu, K., et al. (2013) A Hybrid Fuzzy-Based Personalized Recommender System for Telecom Products/Services. Information Sciences, 235, 117-129. [Google Scholar] [CrossRef] 
[20] Yan, B. and Gregory, S. (2012) Finding Missing Edges in Networks Based on Their Community Structure. Physical Review E, 85, Article ID: 056112. [Google Scholar] [CrossRef] 
[21] Ekstrand, M.D., Riedl, J.T. and Konstan, J.A. (2011) Collaborative Filtering Recommender Systems. Foundations and Trends® in Human—Computer Interaction, 4, 81-173. [Google Scholar] [CrossRef] 
[22] O’Mahony, M.P., Hurley, N.J. and Silvestre, G.C.M. (2006) Detecting Noise in Recommender System Databases. Proceedings of the 11th International Conference on Intelligent User Interfaces, Sydney, 29 January-1 February 2006, 109-115. [Google Scholar] [CrossRef] 
[23] 苏湛, 陈学谦, 艾均, 等. 基于用户相似性选择及标签距离的推荐算法[J]. 应用科学学报, 2023, 41(6): 940-957.
[24] Peng, M., Zeng, G., Sun, Z., et al. (2018) Personalized App Recommendation Based on App Permissions. World Wide Web, 21, 89-104. [Google Scholar] [CrossRef] 
[25] Salloum, S. and Rajamanthri, D. (2021) Implementation and Evaluation of Movie Recommender Systems Using Collaborative Filtering. Journal of Advances in Information Technology, 12, 189-196. [Google Scholar] [CrossRef] 
[26] Ai, J., Cai, Y., Su, Z., et al. (2022) Predicting User-Item Links in Recommender Systems Based on Similarity-Network Resource Allocation. Chaos, Solitons & Fractals, 158, Article ID: 112032. [Google Scholar] [CrossRef] 
[27] Su, Z., Zheng, X., Ai, J., et al. (2020) Link Prediction in Recommender Systems Based on Vector Similarity. Physica A: Statistical Mechanics and Its Applications, 560, Article ID: 125154. [Google Scholar] [CrossRef] 
[28] Lee, S. (2020) Using Entropy for Similarity Measures in Collaborative Filtering. Journal of Ambient Intelligence and Humanized Computing, 11, 363-374. [Google Scholar] [CrossRef] 
[29] Javed, U., Shaukat, K., Hameed, I.A., et al. (2021) A Review of Content-Based and Context-Based Recommendation Systems. International Journal of Emerging Technologies in Learning, 16, 274-306. [Google Scholar] [CrossRef] 
[30] Al-Badarneh, I., Habib, M., Aljarah, I. and Faris, H. (2022) Neuro-Evolutionary Models for Imbalanced Classification Problems. Journal of King Saud University-Computer and Information Sciences, 34, 2787-2797. [Google Scholar] [CrossRef] 

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