|
|
Pure Mathematics 2020
协同过滤算法中一种改进相似度度量的方法
|
Abstract:
信息时代，互联网上的信息量巨大，数据信息给我们的生活带来许多便利的同时，也带来了信息超载问题。协同过滤算法应运而生，作为成功的个性化推荐技术，得到了广泛的应用。它分析用户的行为，通过收集与用户兴趣一致的其他用户的评价信息来产生推荐。然而，传统的推荐算法存在数据稀疏时相似度计算不准确，以及冷启动、可扩展性问题，影响了推荐系统的应用和推广。本文研究了协同过滤推荐技术的基本原理及实现步骤，提出了一种改进的相似度度量方法，可以在不进行复杂计算的情况下，通过提高数据的使用率来很好地提高推荐的准确性。
In the information age, there is a huge amount of information on the Internet. While data infor-mation brings a lot of convenience to our life, it also brings the problem of information overload. Collaborative filtering (CF) algorithm emerges as a successful personalized recommendation technique and is widely used. It analyzes the behavior of users and generates recommendations by collecting the evaluation information of other users who are in line with their interests. However, the traditional recommendation algorithm has some problems such as inaccurate similarity cal-culation when data is sparse, cold start and scalability, which affects the application and promotion of the recommendation system. In this paper, the basic principle and implementation steps of collaborative filtering recommendation technology are studied, and an improved similarity measurement method is proposed, which can improve the accuracy of prediction by improving the utilization rate of data without complex calculation.
| [1] | Aciar, S., Zhang, D., Simoff, S. and Debenham, J. (2007) Informed Recommender: Basing Recommendations on Consumer Product Reviews. IEEE Intelligent Systems, 22, 39-47. https://doi.org/10.1109/MIS.2007.55 |
| [2] | de Campos, L.M., Fernandez-Luna, J.M., Huete, J.F. and Rueda-Morales, M.A. (2010) Combining Content-Based and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks. International Journal of Approximate Reasoning, 51, 785-799. https://doi.org/10.1016/j.ijar.2010.04.001 |
| [3] | 刘平峰, 聂规划, 陈冬林. 基于知识的电子商务智能推荐系统平台设计[J]. 计算机工程与应用, 2007(19): 203-205+220. |
| [4] | Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2001) Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the International Conference on the World Wide Web, New York, 1-5 May 2001, 285-295. https://doi.org/10.1145/371920.372071 |
| [5] | 王成, 朱志刚, 张玉侠, 等. 基于用户的协同过滤算法的推荐效率和个性化改进[J]. 小型微型计算机系统, 2016, 37(3): 30-34. |
| [6] | 李聪, 梁昌勇, 马丽. 基于领域最近邻的协同过滤推荐算法[J]. 计算机研究与发展, 2008, 45(9): 1532-1538. |
| [7] | 吴颜, 沈洁, 顾天竺, 等. 协同过滤推荐系统中数据稀疏问题的解决[J]. 计算机应用研究, 2007, 24(6): 94-97. |
| [8] | 李改, 李磊. 一种解决协同过滤系统冷启动问题的新算法[J]. 山东大学学报(工学版), 2012, 42(2): 11-17. |
| [9] | Ricci, F., Rokach, L. and Shapira, B. (2010) Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B. and Kantor, P., Eds., Recommender Systems Handbook, Springer-Verlag, New York, 1-35. https://doi.org/10.1007/978-0-387-85820-3_1 |
| [10] | Prasad, R. (2012) A Categorical Review of Recommender Systems. International Journal of Distributed and Parallel Systems, 3, 73-83. https://doi.org/10.5121/ijdps.2012.3507 |
| [11] | Adomavicius, G. and Tuzhilin, A. (2005) Toward the Next Genera-tion of Recommender Systems: Toward the Next Generation of Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 17, 734-749. https://doi.org/10.1109/TKDE.2005.99 |
| [12] | 马瑞新, 孟繁成, 王涵杨. 优化的协同过滤推荐算法[J]. 计算机科学与应用, 2011, 1(3): 108-111. |
| [13] | 崔梓凝. 基于协同过滤的推荐算法研究[J]. 数字化用户, 2017, 23(45): 160, 42. |
| [14] | 周军锋, 汤显, 郭景峰. 一种优化的协同过滤推荐算法[J]. 计算机研究与发展, 2004, 41(10): 1842-1847. |
| [15] | Laveti, R.N., Ch, J., Pal, S.N. and Babu, N.S.C. (2016) A Hybrid Recommender System Using Weighted Ensemble Similarity Metrics and Digital Filters. Processings of the 23rd International Conference on High Performance Computing Workshops (HiPCW), Hyderabad, 2016, 32-38. https://doi.org/10.1109/HiPCW.2016.013 |
| [16] | Balabanovic, M. and Shoham, Y. (1997) Fab: Content-Based, Collaborative Recommendation. Communications of the ACM, 40, 66-72. https://doi.org/10.1145/245108.245124 |
