Abstract
Despite case-based reasoning (CBR) being one of the collaborative filtering approaches for a travel advisory system, the application of CBR to travel and tourism is still in a very early stage. Although some researches define a similarity function in CBR by using outranking relationship, there is a lack of research on how and why to combine outranking relationship with CBR. In this research, we address how to combine outranking relationship in the ELECTRE III with CBR for a case-based travel advisory system. In order to demonstrate the effectiveness of the hybrid model, the hybrid model is compared to the situation where the non-hybrid model was applied. In travel advisory mining, data were collected from travelers on the Chachoengsao province. Empirical results show that the hybrid model offers significantly better ranking performance than the CBR model derived from Euclidean metric.
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