| 协同过滤推荐算法研究进展 |
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| 引用本文： | 翁小兰,王志坚. 协同过滤推荐算法研究进展[J]. 计算机工程与应用, 2018, 54(1): 25-31. DOI: 10.3778/j.issn.1002-8331.1710-0081 |
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| 作者姓名： | 翁小兰 王志坚 |
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| 作者单位： | 1.河海大学 计算机与信息学院，南京 2111002.淮阴师范学院 计算机科学与技术学院，江苏 淮安 223300 |
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| 摘 要： | 推荐技术在各个领域得到了广泛的应用，其中协同过滤推荐算法显得尤为突出。从基本概念、工作流程以及评估指标等方面介绍了传统的协同过滤推荐算法，对此类算法存在的数据稀疏性、冷启动、扩展性问题进行了分析，并分类详细归纳了这些问题的研究现状和解决方案；最后提出了协同过滤推荐算法在融合大数据技术、社会网络分析技术以及关键用户分析技术三方面的研究热点。
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| 关 键 词： | 协同过滤 冷启动 稀疏性 扩展性 |
Research process of collaborative filtering recommendation algorithm |
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| WENG Xiaolan,WANG Zhijian. Research process of collaborative filtering recommendation algorithm[J]. Computer Engineering and Applications, 2018, 54(1): 25-31. DOI: 10.3778/j.issn.1002-8331.1710-0081 |
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| Authors: | WENG Xiaolan WANG Zhijian |
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| Affiliation: | 1.College of Computer & Information Engineering，Hohai University，Nanjing 211100, China2.School of Computer Science &Technology，Huaiyin Normal University，Huai’an, Jiangsu 223300, China |
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| Abstract: | Recommended technology is widely applied in various fields, and the successful application of the collaborative filtering recommendation algorithm is especially significant. This paper mainly introduces the basic concept and principle of the collaborative filtering recommendation algorithm, including such aspects: algorithm work flow, recommends process as well as the experiment assessment. The collaborative filtering technique faces up to some problem, although it has achieved great success, because of its algorithm features. The paper analyzes these problems and proposes the corresponding solution of collaborative filtering recommendation algorithm, and finally puts forward the new research hotspots of the collaborative filtering recommendation algorithm. |
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| Keywords: | collaborative filtering cold-start sparsity scalability |
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