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Content Provider | IEEE Xplore Digital Library |
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Author | Zhongbin You Yiwei Sun Yue Chen Ymg Zhang Yangyong Zhu |
Copyright Year | 2006 |
Description | Author affiliation: Dept. of Comput. & Inf. Technol., Fudan Univ., Shanghai (Zhongbin You) |
Abstract | As an important technology transfer media, technology transfer platform (TTP) is being paid extensive attention. Collaborative filtering recommendation method is one of the important recommendation strategies, and is also the most successful one ever applied till now. Traditional collaborative filtering methods, unavoidably, are puzzled by the problem of "data-sparsity"; and in this paper, the intelligent recommendation system is brought forward to settle the problem. The system firstly acquires user's implicit degree-of-interest through analysing Web log; and then combines the implicit degree-of-interest and the explicit rating matrix. On the basis of mixed rating matrix, semantic classifying information is imported to decrease the rating matrix's dimensions and thus further improve the precision of collaborative recommendation. After collaborative recommendation amended, we propose a novel architecture of intelligent recommendation system in TTP. The system has achieved expected effect as it is shown in an experiment |
Starting Page | 4302 |
Ending Page | 4306 |
File Size | 133754 |
Page Count | 5 |
File Format | |
ISBN | 1424403324 |
DOI | 10.1109/WCICA.2006.1713187 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-06-21 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Intelligent systems Collaboration Information filtering Information filters Technology transfer Collaborative work Space technology Information technology Sun Technology management Semantic Classifying Intelligent Recommendation System Collaborative Filtering Technology Transfer Platform (TTP) Implicit Rating |
Content Type | Text |
Resource Type | Article |
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