Title: | incremental learning of triadic plsa for collaborative filtering |
Author: | Wu Hu
; Wang Yongji
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Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Conference Name: | 5th International Conference on Active Media Technology
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Conference Date: | OCT 22-24,
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Issued Date: | 2009
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Conference Place: | Beijing, PEOPLES R CHINA
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Keyword: | Blood vessel prostheses
; Data flow analysis
; Forecasting
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Publisher: | ACTIVE MEDIA TECHNOLOGY, PROCEEDINGS
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Publish Place: | HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
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ISSN: | 0302-9743
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ISBN: | 978-3-642-04874-6
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Department: | Wu, Hu; Wang, Yongji Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
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Sponsorship: | Beijing University of Technology (BJUT); Beijing Municipal Lab of Brain Informatics; Chinese Society of Radiology; National Natural Science Foundation of China (NSFC); State Administration of Foreign Experts Affairs
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English Abstract: | PLSA which was originally introduced in text analysis area, has been extended to predict user ratings in the collaborative filtering context, known as Triadic PLSA (TPLSA). It is a promising recommender technique but the computational cost is a bottleneck for huge data set. We design a incremental learning scheme for TPLSA for collaborative filtering task that could make forced prediction and free prediction as well. Our incremental implementation is the first of its kind in the probabilistic model based collaborative filtering area, to our best knowledge. Its effectiveness is validated by experiments designed for both rating-based and ranking-based collaborative filtering. |
Content Type: | 会议论文
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URI: | http://ir.iscas.ac.cn/handle/311060/8298
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Appears in Collections: | 互联网软件技术实验室 _会议论文
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incremental learning of triadic plsa for collaborative filtering.pdf(298KB) | -- | -- | 限制开放 | -- | 联系获取全文 |
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Recommended Citation: |
Wu Hu,Wang Yongji. incremental learning of triadic plsa for collaborative filtering[C]. 见:5th International Conference on Active Media Technology. Beijing, PEOPLES R CHINA. OCT 22-24,.
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