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A recommender system is basically a type of information filtering system that suggests/recommends items based on the factors that constitute what the user is most interested in. The recommendations are typically provided in relation to different decision-making processes. Tourism is a social phenomenon where people deliberately travel in search of recreation, well-being, cultural exploration or get themselves softened up. But, the amount of information available online keeps expanding at exponential rates and thus the users have expressed their feeling of frustration at how challenging it is to find the appropriate information. This problem is called information overload. This is where the recommendation system comes into play which helps in solving the information overload problem. The hybrid system addresses the disadvantages where location-based recommendation systems are used individually, of which the most notable is the cold start issue. Furthermore, in order to improve the accuracy of the prediction to recommend items, these systems search for the ideal fusion of different approaches. Thus, the hybrid recommendation method solves the challenges like ‘cold start problem’, inability to capture changes in user behavior, sparsity and selecting correct choices for users. This paper explores the hybrid recommendation systems and other filtering techniques used in various fields, their challenges, how they can also be used for tourism recommender systems based on the longitudes and latitudes.