Context Based- Tourism Recommender System: Towards Tourists' Context-Sensitive Preference Conceptual Model

Abstract

With the fact that the Internet facilitates access to information, it will lead to creating information overload, particularly for travelers. It is hard for travelers to find the appropriate destination, and service providers to recommend the suitable destination. The paper aims to propose a context-sensitive preference conceptual model from the tourist's perspectives. Data collection was conducted by examining the literature review. The results show that the proposed solution is to filter information by using a recommender system. The system may need to be conducted by considering the tourists rating, collaboration, and products or services description. Further, the system should consider additional contextual information: location, time, social, and weather. Such a multidimensional context-based recommender system faces problems with the complexity of contextual data with many attributes used for filtering. Therefore, the additional contextual information on a context-based recommender system could be based on the expected contextual preferences. This model is derived from a contextual pre/post-filtering approach or contextual modeling.