By Jesus Favela, Ernestina Menasalvas, Edgar Chávez
This e-book constitutes the refereed complaints of the second one foreign internet Intelligence convention, AWIC 2004, held in Cancun, Mexico, in might 2004. The 22 revised complete papers awarded have been rigorously reviewed and chosen from fifty two submissions. The papers are equipped in topical sections on collaborative filtering and suggestion, assisting net groups, multi-agent structures, delicate computing tools, textual content processing and semantic internet, net details retrieval, and categorization and rating.
Read or Download Advances in Web Intelligence: Second International Atlantic Web Intelligence Conference, AWIC 2004, Cancun, Mexico, May 16-19, 2004. Proceedings PDF
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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
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. 1 Introduction Nowadays web technology has become one of the most important infrastructures for supporting diverse communities; it may be because the Web allows ubiquitous access in a simple way thanks to the HTTP protocol and its addressing scheme , which in turn had foster its growth and had make possible the emergence of web communities.
Most TV programs are only available at the moment of broadcast. For this reason, we combine both measures into the global mean absolute error (gmae); it is the same as mae, but when a prediction technique or strategy cannot make a prediction the default value zero is assumed as the prediction. The lower the gmae the better the accuracy1. 1 Accuracy presented in this paper is based on a rating scale from -1 to +1, where -1 indicates a very strong negative interest and +1 a very strong positive interest; 0 indicates a neutral or indifferent interest.
Rule-based systems are very fast and scalable because they are model based. Since CBR is memory based, the speed of CBR-based strategies depends on the size of the case-bases. In some systems, limiting the size of the case-bases not only improves speed and makes the system more scalable, it can also improve the accuracy of a CBR-based prediction strategy. However, more research is needed to determine under which conditions improved accuracy can be expected with limited case-base sizes. All things considered, we conclude that CBR can indeed be used to create prediction strategies for hybrid recommender systems.