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Titlebook: Web Information Systems Engineering -- WISE 2013; 14th International C Xuemin Lin,Yannis Manolopoulos,Guangyan Huang Conference proceedings

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書目名稱Web Information Systems Engineering -- WISE 2013
副標(biāo)題14th International C
編輯Xuemin Lin,Yannis Manolopoulos,Guangyan Huang
視頻videohttp://file.papertrans.cn/1022/1021520/1021520.mp4
概述Proceedings of the International Conference on Web Information Systems Engineering, WISE 2013
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web Information Systems Engineering -- WISE 2013; 14th International C Xuemin Lin,Yannis Manolopoulos,Guangyan Huang Conference proceedings
描述This book constitutes the proceedings of the 14th International Conference on Web Information Systems Engineering, WISE 2013, held in Nanjing, China, in October 2013. The 48 full papers, 29 short papers, and 10 demo and 5 challenge papers, presented in the two-volume proceedings LNCS 8180 and 8181, were carefully reviewed and selected from 198 submissions. They are organized in topical sections named: Web mining; Web recommendation; Web services; data engineering and database; semi-structured data and modeling; Web data integration and hidden Web; challenge; social Web; information extraction and multilingual management; networks, graphs and Web-based business processes; event processing, Web monitoring and management; and innovative techniques and creations.
出版日期Conference proceedings 2013
關(guān)鍵詞information retrieval; machine learning; recommender systems; semantic Web; social networks
版次1
doihttps://doi.org/10.1007/978-3-642-41230-1
isbn_softcover978-3-642-41229-5
isbn_ebook978-3-642-41230-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

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Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernelsle and interaction information. The proposed model takes an uniformed approach of integrating . with .. We conduct experiments on real-world data sets for typical web mining applications, showing that the proposed model produces better relationship strength measurement in comparison with other social factors.
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Recommending Tripleset Interlinking through a Social Network Approachhis problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90% the number of triplesets to be further inspected for establishing appropriate links.
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Recommending Tripleset Interlinking through a Social Network Approachhis problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90% the number of triplesets to be further inspected for establishing appropriate links.
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