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Titlebook: Adaptive Hypermedia and Adaptive Web-Based Systems; 5th International Co Wolfgang Nejdl,Judy Kay,Eelco Herder Conference proceedings 2008 S

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樓主: fasten
41#
發(fā)表于 2025-3-28 18:15:03 | 只看該作者
Adaptive Hypermedia and Adaptive Web-Based Systems978-3-540-70987-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
42#
發(fā)表于 2025-3-28 21:13:02 | 只看該作者
43#
發(fā)表于 2025-3-29 02:11:35 | 只看該作者
Is There Something You Need to Tell Us?d searchers so as to personalize result-lists to reflect the preferences of the community as a whole. In this paper, we present the results of a recent live-user trial which demonstrates how CWS elicits high levels of participation and how the search activities of a community of related users form a type of social search network.
44#
發(fā)表于 2025-3-29 06:21:14 | 只看該作者
https://doi.org/10.1007/978-3-540-70987-9Web engineering; adaptive Web systems; adaptive hypermedia; adaptive services; contextualization; e-learn
45#
發(fā)表于 2025-3-29 09:54:08 | 只看該作者
978-3-540-70984-8Springer-Verlag Berlin Heidelberg 2008
46#
發(fā)表于 2025-3-29 11:58:23 | 只看該作者
47#
發(fā)表于 2025-3-29 19:08:19 | 只看該作者
48#
發(fā)表于 2025-3-29 22:36:43 | 只看該作者
Vigen Arakelian,Sébastien Briott is the nature of people’s participation in building these repositories? What are their motives? In what ways is their behavior destructive instead of constructive? Motivating people to contribute is a key problem because the quantity and quality of contributions ultimately determine a CALV’s value
49#
發(fā)表于 2025-3-30 01:51:28 | 只看該作者
Vigen Arakelian,Sébastien Briotdia and recommender systems. We show how this personalization framework can be integrated into existing systems by example of the educational online board ., which exploits the framework for recommending relevant discussions to the users. In our evaluations we compare different recommender strategie
50#
發(fā)表于 2025-3-30 07:53:41 | 只看該作者
An Overview of Balancing Methodsd by two users to a set of items are pairwise compared and averaged (correlation). In this paper we make user-to-user similarity adaptive, i.e., we dynamically change the computation depending on the profiles of the compared users and the target item whose rating prediction is sought. We propose to
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