Multi-objective contextual bandits in recommendation systems for smart tourism

In the context of smart tourism, recommender systems play a pivotal role in enhancing the personalization and quality of travel experiences. Tourists often face challenges in decision-making due to information overload. While context-aware recommender systems provide promising solutions by utilizing dynamic contextual data such as time, weather, and location, they struggle to adapt to real-time changes and to balance multiple objectives effectively. To address these challenges, this paper introduces a novel multi-objective contextual multi-armed bandit (MOC-MAB)-based recommender system. This approach integrates the strengths of contextual bandit algorithms with multi-objective optimization to provide personalized recommendations while simultaneously considering relevance and fairness. The proposed system dynamically learns from user feedback to optimize multi-objective recommendations. Extensive experiments conducted on a designed dataset simulating real-world scenarios and the TripAdvisor dataset demonstrate the approach's superior performance in terms of cumulative reward, click-through rate, and regret minimization when compared to baseline methods. This study also illustrates its practical application in the smart tourism context of Marrakesh, showcasing its potential to enhance tourism experiences in smart cities.