Distributed Computing and Artificial Intelligence: 7th by Sigeru Omatu, Masao Fujimura (auth.), Andre Ponce de Leon F.

By Sigeru Omatu, Masao Fujimura (auth.), Andre Ponce de Leon F. de Carvalho, Sara Rodríguez-González, Juan F. De Paz Santana, Juan M. Corchado Rodríguez (eds.)

The foreign Symposium on disbursed Computing and synthetic Intelli-gence (DCAI´10) is an annual discussion board that brings jointly prior adventure, present paintings and promising destiny developments linked to disbursed computing, synthetic intelligence and their software to supply effective recommendations to actual difficulties. the current version has been held on the Polytechnic college of Valencia, from 7 to ten September 2010, in the Congreso Español de Informática (CEDI 2010). expertise move during this box remains to be a problem, with a wide hole among educational examine and commercial items. This version of DCAI goals at contributing to minimize this hole, with a stimulating and efficient discussion board the place those groups can paintings in the direction of destiny cooperation with social and economi-cal benefits.

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A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attraction over the Internet. : Measures of semantic similarity and relatedness for use in ontology-based information retrieval. Thesis of Doctor. : Web Personalization Expert with Combining collaborative Filtering and association Rule Mining Technique. : CMAR. Accurate and efficient classification based on multiple class-association rules. In: Proc. of the IEEE International Conference on Data Mining (ICDM 2001), California, pp.

The main parameters analyzed for the design of the system are two: the number of points of the contours (K), it is used to normalize the contours of different specimens. The value K could go from 2 to 1024, with K very low we only represent values related to basic diameters of the contour. But with K getting high, the number of parameters increases. Another value is L the total number of dimensions on the classification space, it is related with the order of the EFD (N), with the equation L=4·(2·N) -2, moreover it is necessary that N≤K/2.

Sparsity is due to the number of ratings needed for prediction is greater than the number of the ratings obtained because usually collaborative filtering requires user Semantic Based Web Mining for Recommender Systems 19 explicit expression of personal preferences for products. The second limitation is related to performance problems in the search for neighbors in memory-based algorithms. These problems are caused by the necessity of processing large amount of information. The computer time grows linearly with both the number of customers and the number of products in the site.

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