一种改进的更准确的混合推荐算法
An Improved and More Accurate Hybrid Recommendation Algorithm
DOI: 10.12677/AAM.2017.63032, PDF, HTML, XML, 下载: 1,728  浏览: 6,900 
作者: 王全民, 谷实, 李振国, 王开阳, 孙艳峰：北京工业大学，北京
关键词: 个性化；基于内容；协同过滤；推荐算法；Personalization； Content-Based； Collaborative Filtering； Demographic
摘要: 推荐系统可以过滤一些无用信息，可以预测用户是否喜欢给定的资源。基于内容的推荐和协同过滤推荐算法是目前主要的个性化推荐方法。但是随着用户项目的不断增加，用户-项目评分矩阵存在着稀疏性、冷启动等问题。针对此问题，我们提出了一个独特的层叠混合推荐方法，使用评级数据，人口统计数据和特征数据来计算项目之间的相似度。实验表明我们的方法优于传统的推荐系统算法。
Abstract: Recommender system can filter some useless information and can predict whether the users love given resources. Content-based recommendation and collaborative filtering recommendation algorithm is the main personalized recommendation method. However, with the continuous increase of user projects, there are sparse, cold start and other issues in the user-project scoring matrix. In response to this problem, we propose a unique cascade hybrid recommendation method that uses rating data, demographic data, and feature data to calculate the similarity between projects. Experiments show that our method is superior to the traditional recommendation system algorithm.
文章引用：王全民, 谷实, 李振国, 王开阳, 孙艳峰. 一种改进的更准确的混合推荐算法[J]. 应用数学进展, 2017, 6(3): 267-274. https://doi.org/10.12677/AAM.2017.63032

参考文献

[1] Adomavicius, G. (2005) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734-749.
https://doi.org/10.1109/TKDE.2005.99
[2] Breese, J.S., Heckerman, D. and Kadie, C. (1998) Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Morgan Kaufmann, Burlington, Massachusetts, 43-52.
[3] Sarwar, B., Karypis, G., Konstan, J. and Reidl, J. Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 1-5 May 2001, 285-295.
https://doi.org/10.1145/371920.372071
[4] Vozalis, M. and Margaritis, K. (2007) Using SVD and Demographic Data for the Enhancement of Generalized Collaborative Filtering. Information Sciences, 177, 3017-3037.
https://doi.org/10.1016/j.ins.2007.02.036
[5] Burke, R. (2002) Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12, 331-370.
https://doi.org/10.1023/A:1021240730564
[6] Pazzani, M.J. (1999) A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13, 393-408.
https://doi.org/10.1023/A:1006544522159
[7] Pennock, D., Horvitz, E., Lawrence, S. and Giles, C. (2000) Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, 2000, 473-480.
[8] Vozalis, M. and Margaritis, K. (2006) On the Enhancement of Collaborative Filtering by Demographic Data. Web Intelligence and Agent Systems, 4, 117-138.
[9] Jonathan, L.G.T., Herlocker, L., Konstan, J.A. and Riedl, J.T. (2004) Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems (TOIS) Archive, 22, 734-749.
[10] Lang, K. (1995) Newsweeder: Learning to Filter Netnews. Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, 9-12 July 1995, 331-339.
https://doi.org/10.1016/b978-1-55860-377-6.50048-7
[11] Melville, P., Mooney, R.J. and Nagarajan, R. (2002) Content-Boosted Collaborative Filtering for Improved Recommendations. Eighteenth National Conference on Artificial Intelligence, Edmonton, Canada 28 July-1 August 2002, 187-192.

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