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1 Intelligent Computer Entertainment Laboratory Department of Human and Computer Intelligence

Abstract : Recommender systems, in virtual museums and art galleries, providing the personalization and context awareness features require the off-line synthesis of visitors- behaviors therein and the off-line training stage of those synthetic data. This paper deals with the simulation of four visitors- styles, i.e., ant, fish, grasshopper, and butterfly, and the classification of those four styles using an Adaptive Neuro-Fuzzy Inference System ANFIS. First, we analyze visitors- behaviors related to a visit time and an observation distance. Then, the proposed synthesis procedure is developed and used in the off-line training stage of ANFIS. The training and testing data are the average and variance of a set of visitors- attention data computing by the proposed function of the visit time and observation distance variables. Therefore, the trained ANFIS can identify the behavior style of an on-line visitor using the training set of synthetic data and its memberships can describe degrees of uncertainty in behavior styles.

Keywords : 3D virtual environment Recommender system Visitor movement Visitor behavior Visualization Adaptive Neuro-Fuzzy Inference Systems ANFIS





Autor: Kingkarn Sookhanaphibarn - Ruck Thawonmas -

Fuente: https://hal.archives-ouvertes.fr/



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