In the recent times, the massive amount of user-generated data acquired from Internet has become the main source for recommendation generation process in various real-time personalization problems. Among various types of recommender systems, collaborative filtering-based approaches are found to be more effective in generating better recommendations. The recommendation models that are based on this collaborative filtering approach are used to predict items highly similar to the interest of an active target user. Thus, a new hybrid user clustering-based travel recommender system (HUCTRS) is proposed by integrating multiple swarm intelligence algorithms for better clustering. The proposed HUCTRS is experimentally assessed on the large-scale datasets to demonstrate its performance efficiency. The results obtained also proved the potential of proposed HUCTRS over traditional approaches by means of improved user satisfaction. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.