基于合格邻居和异常检测的社区增强协同过滤
Community-Augmented Cosine Collaborative Filtering Based on All Qualified Neighbors and Abnormal Detection
DOI: 10.12677/mos.2024.133196, PDF,    国家自然科学基金支持
作者: 苏 湛, 陈惠鑫, 艾 均：上海理工大学光电信息与计算机工程学院，上海
关键词: 协同过滤；异常检测；相似性网络；社区检测；K-Core分解；Collaborative Filtering； Abnormal Detection； Similarity Network； Community Detection； K-Core Decomposition
摘要: 为了解决协同过滤推荐算法存在较大预测误差和推荐列表准确度不高的问题，提出一种结合异常检测和网络社区并基于所有合格邻居的协同过滤推荐算法。该算法使用修改的拉依达准则检测标记数据异常，在协同过滤相似度计算阶段降低与异常用户之间的相似性权重；使用得到的用户相似性建立网络模型，利用K核分解进行网络社区检测，在得到用户间的社区信息后对社区中用户进行相似性权重处理。基于MovieLens数据集并与五种同类型算法进行对比实验，结果表明，提出的算法可以有效降低预测误差以及提升推荐列表的排序准确度。
Abstract: In order to address the issue of the significant prediction errors and the low accuracy of recommendation lists in the collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm that combines anomaly detection and network community based on all qualified neighbor is proposed. The algorithm uses the modified Pauta criterion for data anomaly detection and mark, and during the collaborative filtering similarity calculation stage reduces the similarity weight with abnormal users; Builds a network model using the obtained user similarity, and uses K-core decomposition for network community detection, and processes the similarity weights for users in the community after obtaining community information between users. Compared with five recommendation algorithms of the same type, and based on the MovieLens dataset, the experimental results showed that the proposed algorithm can effectively reduce prediction error and improve the accuracy of recommendation list rankings.
文章引用：苏湛, 陈惠鑫, 艾均. 基于合格邻居和异常检测的社区增强协同过滤[J]. 建模与仿真, 2024, 13(3): 2133-2146. https://doi.org/10.12677/mos.2024.133196

参考文献

[1] Yu, J., Yin, H., Xia, X., et al. (2023) Self-Supervised Learning for Recommender Systems: A Survey. IEEE Transactions on Knowledge and Data Engineering, 36, 335-355. [Google Scholar] [CrossRef] 
[2] Gao, C., Zheng, Y., Li, N., et al. (2023) A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems, 1, 1-51. [Google Scholar] [CrossRef] 
[3] Sun, N., Luo, Q., Ran, L., et al. (2023) Similarity Matrix Enhanced Collaborative Filtering for E-Government Recommendation. Data and Knowledge Engineering, 145, Article 102179. [Google Scholar] [CrossRef] 
[4] Hu, P., Yang, E., Pan, W., et al. (2022) Federated One-Class Collaborative Filtering via Privacy-Aware Non-Sampling Matrix Factorization. Knowledge-Based Systems, 253, Article 109441. [Google Scholar] [CrossRef] 
[5] Wang, Q., Wu, S., Bai, Y., et al. (2023) Neighbor Importance-Aware Graph Collaborative Filtering for Item Recommendation. Neurocomputing, 549, Article 126429. [Google Scholar] [CrossRef] 
[6] Kuo, R.J. and Li, S.-S. (2023) Applying Particle Swarm Optimization Algorithm-Based Collaborative Filtering Recommender System Considering Rating and Review. Applied Soft Computing, 135, Article 110038. [Google Scholar] [CrossRef] 
[7] Hu, Z., Zhou, X., He, Z., et al. (2023) Discrete Limited Attentional Collaborative Filtering for Fast Social Recommendation. Engineering Applications of Artificial Intelligence, 123, Article 106437. [Google Scholar] [CrossRef] 
[8] Tao, Y., Kong, F., Shi, Y., et al. (2023) Efficient, Secure and Verifiable Outsourcing Scheme for SVD-Based Collaborative Filtering Recommender System. Future Generation Computer Systems, 149, 445-454. [Google Scholar] [CrossRef] 
[9] Su, Z., Huang, Z., Ai, J., et al. (2022) Enhancing the Scalability of Distance-Based Link Prediction Algorithms in Recommender Systems through Similarity Selection. PLOS ONE, 17, e0271891. [Google Scholar] [CrossRef] [PubMed]
[10] Yang, X. and Li, X. (2023) ATDAD: One-Class Adversarial Learning for Tabular Data Anomaly Detection. Computers and Security, 134, Article 103449. [Google Scholar] [CrossRef] 
[11] Rodríguez, M., Tobón, D.P. and Múnera, D. (2023) Anomaly Classification in Industrial Internet of Things: A Review. Intelligent Systems with Applications, 18, Article 200232. [Google Scholar] [CrossRef] 
[12] Kumar, A., Parkash, C., Tang, H., et al. (2023) Intelligent Framework for Degradation Monitoring, Defect Identification and Estimation of Remaining Useful Life (RUL) of Bearing. Advanced Engineering Informatics, 58, Article 102206. [Google Scholar] [CrossRef] 
[13] Ai, J., Cai, Y., Su, Z., et al. (2022) Predicting User-Item Links in Recommender Systems Based on Similarity-Network Resource Allocation. Chaos, Solitons and Fractals, 158, Article 112032. [Google Scholar] [CrossRef] 
[14] Ai, J., Liu, Y., Su, Z., et al. (2019) Link Prediction in Recommender Systems Based on Multi-Factor Network Modeling and Community Detection. Europhysics Letters, 126, Article 38003. [Google Scholar] [CrossRef] 
[15] Ai, J., Liu, Y., Su, Z., et al. (2021) K-Core Decomposition in Recommender Systems Improves Accuracy of Rating Prediction. International Journal of Modern Physics C, 32, Article 2150087. [Google Scholar] [CrossRef] 
[16] He, X.-S., Zhou, M.-Y., Zhuo, Z., et al. (2015) Predicting Online Ratings Based on the Opinion Spreading Process. Physica A: Statistical Mechanics and Its Applications, 436, 658-664. [Google Scholar] [CrossRef] 
[17] 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 125154. [Google Scholar] [CrossRef] 
[18] Lee, S. (2020) Using Entropy for Similarity Measures in Collaborative Filtering. Journal of Ambient Intelligence and Humanized Computing, 11, 363-374. [Google Scholar] [CrossRef] 
[19] Singh, P.K., Sinha, M., Das, S., et al. (2020) Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering Using Bhattacharyya Coefficient and Most Similar Item. Applied Intelligence, 50, 4708-4731. [Google Scholar] [CrossRef] 
[20] Su, Z., Yang, H. and Ai, J. (2023) FPLV: Enhancing Recommender Systems with Fuzzy Preference, Vector Similarity, and User Community for Rating Prediction. PLOS ONE, 18, e0290622. [Google Scholar] [CrossRef] [PubMed]

为你推荐