In recent times, Recommender Systems (RSs) are gaining immense popularity with the wider adaptation to deal information overload problem in various application domains such as e-commerce, entertainment, e-tourism, etc. RSs are developed as information filtering systems to make personalized predictions based on the priorities and preferences for the suggestion of relevant items to users. Travel Recommender Systems (TRSs) generates a list of best matching locations or Point of Interests (POIs) to the users based their preferences. Predicting interesting locations for the generation recommendations from Location Based Social Network (LBSN) is crucial due to variety, size, and dimensions of data. The growing demand for effective TRS extends the scope for the development of user behavior based recommendation approach. In the literature, several research works are conducted to generate location recommendations by focusing on location attributes and failed to incorporate user behavior. As a significant solution to the existing limitations of TRSs, we propose Activity and Behavior induced Personalized Recommender System (ABiPRS) as a hybrid approach to predict persuasive POI recommendations. The proposed ABiPRS is designed to support travelling user by providing effective list of POIs as recommendations. As an extension, we have designed a new group recommendation model to meet the requirements of the group of users by exploiting relationships between them. Further, we have developed a novel hybridization approach for aggregating recommendations from multiple RSs to improve the effectiveness of recommendations. The proposed approaches are evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed hybrid recommendation approach over standalone and baseline hybrid approaches. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.