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Titlebook: Web Information Systems Engineering – WISE 2015; 16th International C Jianyong Wang,Wojciech Cellary,Yanchun Zhang Conference proceedings 2

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41#
發(fā)表于 2025-3-28 18:33:31 | 只看該作者
42#
發(fā)表于 2025-3-28 20:13:35 | 只看該作者
A Soft Subspace Clustering Method for Text Data Using a Probability Based Feature Weighting Scheme,d different clusters in subspaces using a weighted distance measure. The weighting scheme heavily affects the clustering performance and requires special consideration. Since text data has semantic information along with syntactic information, a weighting scheme, which uses semantic information, is
43#
發(fā)表于 2025-3-28 23:11:18 | 只看該作者
A Web-Based Application for Semantic-Driven Food Recommendation with Reference Prescriptions,systems, since it often has educational purposes, to improve behavioural habits of users. In this paper, we discuss the application of Semantic Web technologies in a menu generation system, that uses a recipe dataset and annotations to recommend menus according to user’s preferences. Reference presc
44#
發(fā)表于 2025-3-29 06:09:39 | 只看該作者
45#
發(fā)表于 2025-3-29 08:13:45 | 只看該作者
Incorporating Cohesiveness into Keyword Search on Linked Data,query language and the structure of the data. However, the imprecision of keyword queries results in overwhelming numbers of candidate results making the identification of relevant results challenging and hindering the scalability of the query evaluation algorithms..To address these issues, we intro
46#
發(fā)表于 2025-3-29 12:33:33 | 只看該作者
47#
發(fā)表于 2025-3-29 16:11:13 | 只看該作者
Privacy-Enhancing Range Query Processing over Encrypted Cloud Databases,ate information and the cloud servers may not be fully trusted, it is desirable to encrypt the data before outsourcing and as a result, the functionality and efficiency has to be sacrificed. In this paper, we propose a privacy-enhancing range query processing scheme by utilizing polynomials and kNN
48#
發(fā)表于 2025-3-29 21:50:40 | 只看該作者
49#
發(fā)表于 2025-3-30 03:55:33 | 只看該作者
50#
發(fā)表于 2025-3-30 08:02:54 | 只看該作者
Cross-Domain Collaborative Recommendation by Transfer Learning of Heterogeneous Feedbacks, by mining useful knowledge from massive data. The big data is often multi-source and heterogeneous, which challenges the recommendation seriously. Collaborative filtering is the widely used recommendation method, but the data sparseness is its major bottleneck. Transfer learning can overcome this p
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