Personalized Group Itinerary Recommendation using a Knowledge-based Evolutionary Approach

Abstract

The problem of recommending a group itinerary is considered to be NP-hard and can be defined as an optimization problem. The goal is to recommend the best series of points of interest (POIs) to a group of people who are visiting a destination based on their preferences and past experiences. This paper proposes an evolutionary approach based on cultural algorithms to address this problem. Our objective is to maximize the group's satisfaction by recommending an itinerary comprised of the optimal series of visiting POIs, considering the interests of all members, total travel time, and visit duration while minimizing the travel costs within their assigned budget. The proposed algorithm uses historical and normative knowledge to create a belief space used later to guide the search direction and decision-making. The belief space is a knowledge repository that tracks the evolution of decisions during the search process. We evaluated the performance of the proposed algorithm on a set of real-world datasets and compared that with state-of-the-art approaches. We also conducted non-parametric tests to analyze the results. Compared with other algorithms, the proposed approach is capable of recommending efficient and satisfactory itineraries to groups with diverse interests.