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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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31#
發(fā)表于 2025-3-26 21:23:21 | 只看該作者
32#
發(fā)表于 2025-3-27 04:54:16 | 只看該作者
33#
發(fā)表于 2025-3-27 08:40:29 | 只看該作者
Exploiting User Queries for Search Result Clustering the user, the different cluster should contain the results corresponding to different possible meanings of the user query and the cluster labels should reflect these meanings. However, existing SRC algorithms often ignore the user query and group the results based just on the similarity of search r
34#
發(fā)表于 2025-3-27 12:09:24 | 只看該作者
Exploiting User Queries for Search Result Clustering the user, the different cluster should contain the results corresponding to different possible meanings of the user query and the cluster labels should reflect these meanings. However, existing SRC algorithms often ignore the user query and group the results based just on the similarity of search r
35#
發(fā)表于 2025-3-27 14:56:53 | 只看該作者
36#
發(fā)表于 2025-3-27 21:34:02 | 只看該作者
Towards Context-Aware Social Recommendation via Trust Networkstion models cannot well handle the heterogeneity and diversity of the social relationships (e.g., different friends may have different recommendations on the same items in different situations). Furthermore, few models take into account (non-social) contextual information, which has been proved to b
37#
發(fā)表于 2025-3-28 01:07:31 | 只看該作者
Personalized Recommendation on Multi-Layer Context Graphecommender systems, but most existing approaches only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a Multi-Layer Context Graph (MLCG) model which incorporates a variety of contextual information into a recom
38#
發(fā)表于 2025-3-28 02:17:14 | 只看該作者
39#
發(fā)表于 2025-3-28 08:23:08 | 只看該作者
Recommending Tripleset Interlinking through a Social Network Approachipleset is a non-trivial task in the publishing process. Without prior knowledge about the entire Web of Data, a data publisher must perform an exploratory search, which demands substantial effort and may become impracticable, with the growth and dissemination of Linked Data. Aiming at alleviating t
40#
發(fā)表于 2025-3-28 12:46:20 | 只看該作者
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