Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism

Abstract

In the context of smart tourism, the utilization of recommender systems is becoming increasingly critical for enhancing the personalization and quality of travel experiences. Tourists often encounter complex decision-making due to information overload, context-aware recommender systems have emerged as a promising solution, leveraging contextual data such as time, weather, and location. However, these systems face the challenge of the complexity of handling dynamic context. Thus, the static nature of these systems can result in a degradation of performance, as they fail to capture the dynamic nature of user behavior and context. Addressing these issues, this paper presents a novel multi-objective contextual multi-armed bandit-based recommender system. This proposal integrates the strengths of contextual bandit algorithms with multi-objective optimization, offering personalized recommendations and learning from user feedback. The multi-objective optimization includes the dual necessities of relevance and fairness in recommendations, ensuring the promotion of a balanced tourism ecosystem. Extensive experiments were carried out on public datasets to evaluate the performance of our proposed approach. Its effectiveness was compared with baseline methods to establish its performance, demonstrating the significance of multi-objective optimization in enhancing personalized recommendations in smart tourism. To evaluate the performance of our proposed algorithm, we conducted experiments using two datasets, a designed dataset that simulates real-world scenarios and TripAdvisor dataset. The study provides a case scenario of implementing this proposed approach in the smart tourism context of Marrakesh, demonstrating its potential to revolutionize the tourist experience in smart cities.