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Titlebook: User Modeling, Adaptation, and Personalization; 17th International C Geert-Jan Houben,Gord McCalla,Massimo Zancanaro Conference proceedings

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發(fā)表于 2025-3-28 17:12:35 | 只看該作者
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發(fā)表于 2025-3-28 21:21:57 | 只看該作者
0302-9743 ; adaption and personalization for Web 2.0; lifelong user modelling; personalization in mobile and pervasive computing; ubiquitous user modeling; user-centred design and evaluation of adaptive systems.978-3-642-02246-3978-3-642-02247-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
43#
發(fā)表于 2025-3-29 00:08:54 | 只看該作者
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發(fā)表于 2025-3-29 03:07:11 | 只看該作者
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發(fā)表于 2025-3-29 10:30:16 | 只看該作者
Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Damount of corresponding data. We asked subjects the same question about preference both in the real and the supposed situation. Then we proposed a new model construction method using a difference model constructed from the correspondence data and showed the effectiveness through the experiments.
46#
發(fā)表于 2025-3-29 11:36:47 | 只看該作者
47#
發(fā)表于 2025-3-29 15:59:28 | 只看該作者
Social Computers for the Social Animal: State-of-the-Art and Future Perspectives of Social Signal Prore than human." This is more than an abstract philosophical statement if, twenty five centuries after, we observe that people have exactly the same social behavior whether they interact with a computer or with another person. Furthermore, there is evidence that users tend to appreciate more compute
48#
發(fā)表于 2025-3-29 21:06:56 | 只看該作者
49#
發(fā)表于 2025-3-30 00:27:20 | 只看該作者
Challenges for the Multi-dimensional Personalised Websser extent in open corpus content. From a commercial perspective, web adaptivity has been more focused on adaptive content retrieval rather than adaptive content composition. However, personal use of the web extends far beyond just content, and encompasses many dimensions which need to be addressed
50#
發(fā)表于 2025-3-30 06:41:58 | 只看該作者
Modeling User Affect from Causes and Effectsigh level of uncertainty involved in recognizing a variety of user emotions by probabilistically combining information on both the causes and effects of emotional reactions. In previous work, we presented the performance and limitations of the model when using only causal information. In this paper,
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