| Personalized Service System Based on Hybrid Filtering for Digital Library |
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| 作者姓名： | 高凤荣 邢春晓 杜小勇 王珊 |
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| 作者单位： | [1]Web and Software Technology R＆D Center, Research Institute of Information Technology, Tsinghua University, Beijing 100084, China [2]School of Information, Renmin University of China, Beijing 100872, China |
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| 摘 要： | Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.
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| 关 键 词： | 数字图书馆 人性化服务系统 用户偏爱模型 综合筛选 |
| 收稿时间： | 17 May 2005 |
| 修稿时间： | 2005-05-17 |
Personalized Service System Based on Hybrid Filtering for Digital Library |
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| GAO Fengrong,XING Chunxiao,DU Xiaoyong,WANG Shan.Personalized Service System Based on Hybrid Filtering for Digital Library[J].Tsinghua Science and Technology,2007,12(1):1-8. |
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| Authors: | GAO Fengrong XING Chunxiao DU Xiaoyong WANG Shan |
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| Institution: | 1. College of Data Science and Application, Inner Mongolia University of Technology, Huhhot 010080, China;1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;2. Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan 430070, China;3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;4. School of Mechanical Engineering, University of Birmingham, Birmingham, B15 2TT, UK |
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| Abstract: | Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach. |
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| Keywords: | personalized service system content-based filtering collaborative filtering user preferences model category-based collaborative filtering meta-information filtering |
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