KomoTrip: a multi-day travel itinerary recommendation method based on the discrete komodo mlipir algorithm

Abstract

Technological developments in recent years led to the emergence of increasingly sophisticated recommender systems to support multi-day travel itineraries that fall under the Tourist Trip Design Problem (TTDP). Various problem analogies are widely used to solve TTDP, such as Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), Orienteering Problem (OP), and Team Orienteering Problem with Time Windows (TOPTW). For multi-day route recommendation, TOPTW is suitable as a problem analogy since there is a per-day travel duration constraint. So far, TTDP with TOPTW does not consider the weighting (priority level of users) for each requirement attribute in a multi-attribute-based TOPTW to ensure personalized recommendations. In addition, running time remains a challenge in many studies in the TOPTW area. Many metaheuristic algorithms have been adopted to TOPTW for generating a time-efficient approach. Komodo Mlipir Algorithm (KMA) emerges as a new algorithm that promises good scalability. Therefore, we propose KomoTrip, a method that adopts the discrete version of KMA and Multi-Attribute Utility Theory (MAUT) to recommend optimal travel routes per day by accommodating the multi-attribute preferences of users. We perform three evaluation scenarios, i.e., general performance, Degree of Interest (DOI) combinations, and varying numbers of Points of Interest (POI), consistently demonstrating that KomoTrip outperforms several benchmark algorithms in terms of computational time efficiency and also exhibits robust fitness values across different problem dimension scales. Thus, KomoTrip can be regarded as an efficient algorithm to recommend optimal multi-day tour routes, effectively incorporating weighted multi-attribute preferences into its optimization process. We further benchmarked KomoTrip against state-of-the-art TOPTW heuristics on the public Solomon dataset, where it demonstrated competitive profit values, particularly for a larger number of days (tours), and consistently achieved superior runtime performance.