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.

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

“…In [89] is stated that the majority of the existing tourism recommender systems use content and knowledgebased approaches, which suffer from the 'cold start' problem and need enough historical rating and extra knowledge data, thus exposing a recommendation system that categorizes the tourists using their demographic information and then makes recommendations based on demographic classes by using naive Bayes, Bayesian networks and SVMs. Additionally, in [91] is exposed that tourism services are highly context-sensitive, this being one of the reasons to develop a tourism attraction recommender system based on context. Authors in [94] also developed a contextaware recommender system based on an improved naive Bayes algorithm.…”

Improving Music Recommendation System by Applying Latent Topics of Lyrics

“…It encounters the cold start problem which occurs when there is no enough user input or no user input. In recent research work, researchers address a hybrid approach by combining content-based filtering with collaborative filtering [16] [17], to minimize the drawback of each collaborative and content based filtering techniques. The meaningful characteristic of user's content plays a vital role to provide the weakness of collaborative filtering.…”