| 基于多粒度特征和混合算法的文档推荐系统 |
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| 引用本文： | 邬登峰,白琳,王涛,李慧,许舒人.基于多粒度特征和混合算法的文档推荐系统[J].计算机系统应用,2018,27(3):9-17. |
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| 作者姓名： | 邬登峰 白琳 王涛 李慧 许舒人 |
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| 作者单位： | 中国科学院大学, 北京 100049;中国科学院 软件研究所 软件工程技术研究开发中心, 北京 100190,中国科学院 软件研究所 软件工程技术研究开发中心, 北京 100190,北京智识企业管理咨询有限公司, 北京 100101,中国科学院 软件研究所 软件工程技术研究开发中心, 北京 100190,中国科学院 软件研究所 软件工程技术研究开发中心, 北京 100190 |
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| 基金项目： | 北京市科技计划项目(D171100003417002) |
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| 摘 要： | 文库系统对信息的传播利用有着重要的作用, 但在文库系统中出现信息过载问题后, 数据的利用率会大大降低. 针对该问题提出了一种基于多粒度特征和混合算法的文档推荐系统, 系统在短语和词语两个粒度上对用户兴趣及文档特征进行建模, 综合基于内容推荐算法及协同过滤算法, 为用户生成兴趣列表. 系统测试数据表明, 系统在准确率、召回率、覆盖率、新颖度等指标上均有较为优异的表现, 其为用户推荐的文档较符合用户实际偏好, 有助于提升文库系统的数据利用率, 改善用户体验.
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| 关 键 词： | 用户兴趣模型 文档特征 基于内容推荐 协同过滤 推荐系统 |
| 收稿时间： | 2017/6/12 0:00:00 |
| 修稿时间： | 2017/6/27 0:00:00 |
Document Recommendation System Based on Multi-Granularity Features and Hybrid Algorithms |
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| WU Deng-Feng,BAI Lin,WANG Tao,LI Hui and XU Shu-Ren.Document Recommendation System Based on Multi-Granularity Features and Hybrid Algorithms[J].Computer Systems& Applications,2018,27(3):9-17. |
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| Authors: | WU Deng-Feng BAI Lin WANG Tao LI Hui and XU Shu-Ren |
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| Affiliation: | University of Chinese Academy of Sciences, Beijing 100049, China;Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China,Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China,Beijing KM Consulting Co. Ltd., Beijing 100101, China,Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China and Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | Document System plays an important role in information dissemination and utilization. However, with the emergence of information overload, the utilization rate of data would greatly decrease. To solve this problem, a document recommendation system based on multi-granularity features and Hybrid Algorithms is proposed. User interest and document feature models are established on both phrase and term granularities. Then, the system generates recommendation lists for users based on the combination of content-based and collaborative-filtering algorithms. The tests based on authentic data demonstrate that the document recommendation system has a better performance on precision, recall rate, coverage rate and novelty. The recommendation lists are more in line with users'' interests. This helps to increase the utilization rate of data and improves user experience with better performance. |
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| Keywords: | user interest model document feature content-based recommendation collaborative-filtering recommendation system |
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