The development of internet technologies has brought digital services to the hands of common man. In the selection process of relevant digital services to the active target user, recommender systems have proved its efficiency as a successful decision support tool. Among many successful techniques incorporated to generate recommendations, collaborative filtering has been widely used to make similarity-based predictions for the recommendation of the relevant list of items to the users. As an advancement, utilizing clustering mechanisms with collaborative filtering for grouping similar users as clusters can enhance the efficiency of the recommendation generated. Though many clustering mechanisms have been employed to group similar users in the existing works, incorporation of bio-inspired clustering has yet to be explored for the generation of optimal recommendations. In this paper, a novel user clustering approach based on Quantum-behaved Particle Swarm Optimization (QPSO) has been proposed for the collaborative filtering based recommender system. The proposed recommendation approach has been evaluated on real-world large-scale datasets of Yelp and TripAdvisor for hit-rate, precision, recall, f-measure, and accuracy. The obtained results illustrate the advantageous performance of proposed approach over its peer works of recent times. We have also developed a new mobile recommendation framework XplorerVU for the urban trip recommendation in smart cities, to evaluate the proposed recommendation approach and the real-time implementation details of the mobile application in the smart-cities are also presented. The evaluation results prove the usefulness of the generated recommendations and depict the users’ satisfaction on the proposed recommendation approach. © 2017 Elsevier B.V.