A NOVEL DETERMINING APPROACH FOR TRAVEL PACKAGE RECOMMENDATION

Abstract

Modern years have mark an growing concern in acceptance systems. In spite of important growth in this field, there still remain numerous Way to probe in truth, this paper provides a study of utilizing 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. To that end, in this paper, we first study the feature of the persist travel packages and create the more advance effective state a tourist-area-season topic (TAST) model. This TAST model can present travel packages and tourists by different topic distributions, The topic extraction is based on some rule and the tourists and the native characteristic (i.e., locations, travel seasons) of the landscapes. Then, based on this document is about a particular topic we suggest a cocktail approach to generate the for personify travel package recommendation. Furthermore, we extend the similarity between tourist and the tourist interest information i.e TAST model to the tourist-relation-area-season topic collaborative pricing and relational area based on similarity of tourist (TRAST) model for capturing the apparent association among the tourists in each travel group.