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Titlebook: Web Information Systems and Technologies; 12th International C Valérie Monfort,Karl-Heinz Krempels,Paolo Traverso Conference proceedings 20

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41#
發(fā)表于 2025-3-28 18:14:45 | 只看該作者
Gregor Blichmann,Carsten Radeck,Robert Starke,Klaus Mei?nerry production of goods, and growing consumerism. Indeed, many of the case studies undertaken to date deal with highly urbanized, and often industrialized, contexts (e.g., Henry, i991; L.eeDecker, 1993; but see Lees and Majewski, 1993, and McBride and McBride, 1987, for rural examples).
42#
發(fā)表于 2025-3-28 20:20:13 | 只看該作者
43#
發(fā)表于 2025-3-29 02:01:55 | 只看該作者
44#
發(fā)表于 2025-3-29 05:27:49 | 只看該作者
1865-1348 d papers from ?the 12th International Conference on Web Information Systems and Technologies, WEBIST 2016, held in Rome, Italy, April 23-25, 2016, organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC)..The purpose of the WEBIST series of conferen
45#
發(fā)表于 2025-3-29 09:05:16 | 只看該作者
46#
發(fā)表于 2025-3-29 13:05:24 | 只看該作者
Customizable Web Services Matching and Ranking Tool: Implementation and Evaluation,ropriate Web service among the different available candidates. Different matchmaking frameworks are now available in the literature but most of them present at least one of the following shortcomings: (i) use of strict syntactic matching; (ii) use of capability-based matching; (iii) lack of customiz
47#
發(fā)表于 2025-3-29 17:10:39 | 只看該作者
48#
發(fā)表于 2025-3-29 20:36:58 | 只看該作者
49#
發(fā)表于 2025-3-30 02:49:52 | 只看該作者
Subtopic Ranking Based on Block-Level Document Analysis,ind their original query. Information on subtopics are useful for search systems to generate diversified search results. Search result diversification is important when there are multiple ways to interpret the submitted query. In search result diversification, it is important to rank subtopics by th
50#
發(fā)表于 2025-3-30 04:06:04 | 只看該作者
Improving Serendipity and Accuracy in Cross-Domain Recommender Systems,f items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly avail
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