Semantic Approach for Travel Package and Recommendation

In previous years we have seen an amplified interest in recommender systems. In spite of significant progress in this field, there still remain several avenues to explore. Indeed, this paper provides a study of exploiting online travel information for Customized 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. To that end, in this paper, we first examine the characteristics of the prevailing travel packages and develop a tourist-area season topic (TAST) model. This TAST model can represent travel packages in detail and tourists by different topic distributions, where the topic withdrawal is conditioned on both the tourists and the basic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a Semantic Web site roach to generate the lists for Customized travel package recommendation. Furthermore, we also extend the TAST model to the tourist-relationarea- season topic (TRAST) model for capturing the latent interaction among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the Semantic recommendation Web site roach on the real-world travel package data. Experimental results show that the TAST model can effectually capture the unique characteristics of the travel data and the Semantic Web site roach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist interaction, the TRAST model can be used as an effective assessment for travel group formation.