| 基于用户特征的分步协同推荐算法* |
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| 引用本文： | 黄文明,程广兵,邓珍荣,周先亭.基于用户特征的分步协同推荐算法*[J].计算机应用研究,2017,34(4). |
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| 作者姓名： | 黄文明 程广兵 邓珍荣 周先亭 |
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| 作者单位： | 桂林电子科技大学广西可信软件重点实验室,桂林电子科技大学计算机科学与工程学院,桂林电子科技大学广西可信软件重点实验室,桂林电子科技大学计算机科学与工程学院 |
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| 基金项目： | 信息网格的可信访问控制研究(编号：kx201106),广西可信软件重点实验室基金;2015年广西科技攻关项目(编号：桂科攻1598019-6 )。 |
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| 摘 要： | 协同过滤是解决信息过载问题的一种有效技术。然而基于内存的推荐面临着可扩展性问题,基于模型的推荐需要训练大量的参数。本文提出了基于用户特征的K-means用户聚类算法,然后用分步协同过滤框架融合基于项目和基于用户的协同过滤给每一个聚簇训练一个模型。实验结果表明本文提出的算法能极大的提高推荐精度,同时在一定程度上解决了基于模型和基于内存的推荐存在的不足。
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| 关 键 词： | 协同过滤 相似性度量 用户特征 K-means 模型 |
| 收稿时间： | 2016/3/7 0:00:00 |
| 修稿时间： | 2017/3/9 0:00:00 |
Distributed collaborative recommendation algorithm based on user characteristics |
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| Huaang Wenming,Cheng Guangbing,Deng Zhenrong and Zhou Xianting.Distributed collaborative recommendation algorithm based on user characteristics[J].Application Research of Computers,2017,34(4). |
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| Authors: | Huaang Wenming Cheng Guangbing Deng Zhenrong and Zhou Xianting |
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| Affiliation: | Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology,School of Computer Science and Engineering, Guilin University of Electronic Technology,Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology,School of Computer Science and Engineering, Guilin University of Electronic Technology |
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| Abstract: | Collaborative Filtering is an effective technique addressing the information overload problem. However,the Memory-Based Recommendation suffers from difficulty in scalability. Model-Based Recommendation have a multitude of parameters to train, the paper proposes K-means user-clustering algorithm based on user characteristics. Then, by using distributed Collaborative Filtering framework mixed with Item-Based and User-Based Collaborative Filtering to train a model to every cluster. The experimental result shows that the algorithm proposed in this paper can greatly improve the recommendation precision. And solves the limitations of Model-Based and Memory-Based recommendations to a certain extent at the same time. |
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| Keywords: | collaborative filtering similarity measurement user characteristics K-means model |
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