Advances in Web Intelligence: Second International Atlantic by Jesus Favela, Ernestina Menasalvas, Edgar Chávez

By Jesus Favela, Ernestina Menasalvas, Edgar Chávez

This publication constitutes the refereed lawsuits of the second one overseas net Intelligence convention, AWIC 2004, held in Cancun, Mexico, in could 2004. The 22 revised complete papers awarded have been rigorously reviewed and chosen from fifty two submissions. The papers are geared up in topical sections on collaborative filtering and suggestion, aiding internet groups, multi-agent platforms, delicate computing tools, textual content processing and semantic internet, internet info retrieval, and categorization and score.

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Additional info for Advances in Web Intelligence: Second International Atlantic Web Intelligence Conference, AWIC 2004, Cancun, Mexico, May 16-19, 2004. Proceedings

Example text

To achieve these requirements, we define and use the following mechanisms: fragmentation, access control, concurrency control, replication, automatic updating and virtual naming of documents and resources. These mechanisms are developed and mapped into the system architecture of the PIÑAS platform, which is further discussed in the following subsection. 2 Architecture The architecture of the PIÑAS platform is based on a three layer distributed model that integrates key components to provide support for cooperative authoring on the Web (see Fig.

As users are more mobile, distributed or belongs to various communities, this feature could overwhelm them and hinder interactivity. Our aim is to provide web applications with a proactive approach, where users can be reached through different devices seamlessly. In order to do so, it is important to determine events relevance and users availability preferences for multiple contexts. The proposal is enabled as a multi-agent system (MAS) where agents reason based on ontologies and direct event notification trough different channels or devices.

We hypothesize that the removal of old cases made the strategy more accurate, since these cases represented old situations that did not occur again in the system. Furthermore, some prediction techniques behave differently early on in a system than they do later on, even under the same conditions according to the validity indicators. For example, in two situations A and B, a validity indicator of social filtering indicates that there are 40 similar users that have rated the item for which a prediction is necessary; however, in situation A, early in the system, the similarity of these 40 users is based on less rated items by each user than in the later situation B; hence the probability that social filtering provides an accurate prediction is higher in situation B than in A.

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