Linking multidimensional context to support tourism recommender system

The trend of the utilization of information technology has made it easier for service providers and users to share information. However, the trade-off eases the generating of excessive information. This makes it difficult to search, sort, and select the information. Therefore, to cope with the excessive information, filtering through the recommendation system is needed. Two-dimensional recommendation system defines the interaction users and items, however, it has several limitations, such as cold start problem, limited content analysis, sparsity, and scalability. To overcome this, the solution is a recommendation of a multidimensional approach by taking into account the additional contextual information including location, time, and social activities. The preparation of the multidimensional recommendation system is done through a literature review. Related to tourism context, a conceptual model of multidimensional context-based recommendation systems, such as tourist (user), destination (item), location, time, social, and the weather is proposed.