Personalized e-tourism attraction recommendation based on context

Abstract

Based on analysis of mobile tourism users' multi-dimensional feature, the concept of context is introduced into user model modeling of mobile tourism. From the perspective of user and context, context theory and machine learning is used to accomplish user modeling in terms of tourism activities recommendation. The dimension of this model includes history behavior, current context, historical context and demographic factor. The problems of new user and similar recommendation and lack of weight are settled in this paper. According to the impact of multi dimension to user preference, user preference interfering is used to acquire user preference to accomplish multi-dimensional user model based on context model to contribute to improvement of traditional e-tourism recommendation and personalization and adaptability of platform.