Abstract: The online travel information imposes an increasing challenge for tourists who have to choose from a large number of available travel packages for satisfying their personalized needs. So we have to exploiting effective way of online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. In this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features such as locations travel seasons of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation which follows a hybrid recommendation strategy and has the ability to combine many possible constraints that exist in the real-world scenarios. We extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group.
Keywords: Tourist-Relation-Area-Season Topic (TRAST), Collaborative filtering (CF), Latent Dirichlet allocation (LDA), Advance RISC Machine (ARM).