With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations. Copyright © 2019, IGI Global.