A Genetic Algorithm for Generating Travel Itinerary Recommendation with Restaurant Selection

Wibowo, Budhi Sholeh; Handayani, Marta Tika

doi:10.1109/ieem.2018.8607677

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…A genetic algorithm was also proposed in [10] to generate tourist itineraries that include not only touristic attractions but also restaurants, with the additional constraint that they should be visited at lunch or dinner time.…”

Section: Literature Analysismentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers

et al. 2023

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The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city’s infrastructure. An experimental evaluation was conducted considering Naples’s fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19∗1012 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps.

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Section: Literature Analysismentioning

confidence: 99%

Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers

et al. 2023

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“…As a result, the algorithm managed to produce accurate results of recommendation to the users as the accuracy was evaluated using the Mean Absolute Error (MAE) value. Wibowo and Handayani (2018) applied the GA for a restaurant RS by generating a travel itinerary, as an experimental study to produce a high-quality itinerary consisting of an efficient route to visit the recommended restaurants at a proper time.…”

Section: Related Workmentioning

confidence: 99%

The The Efficiency of Hybridised Genetic Algorithm and Ant Colony Optimisation (HGA-ACO) in a Restaurant Recommendation System

Kassim¹

2022

ASMScJ

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A recommendation system (RS) is used to provide recommendations to users by filtering items based on given inputs. Metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimisation (ACO) are known to be used in many RS to provide optimal and good recommendations. Both algorithms are designed based on nature-inspired events, where GA is designed based on the natural evolution process while ACO is based on ants’ behaviour in their natural habit. In this paper, both GA and ACO algorithms were implemented in a restaurant RS and evaluated by using the restaurant’s attributes, which was then followed by a list of recommended restaurants as the output. With the highest score of 99.64% of accuracy, GA overtakes ACO in terms of recommendation accuracy while ACO computed 67.12% lesser runtime than GA. Considering the results acquired, a new hybrid framework known as the HGA-ACO algorithm was proposed. The proposed HGA-ACO has a recommendation accuracy of 99.57% and achieved a 31.37% runtime reduction from GA. Thus, the proposed framework was observed to have improved the output accuracy of ACO and improved the processing time in GA, thus, improving the overall efficiency of the RS.

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“…Based on user interests and trip constraints, Liu et al [18] applied GA to the real-time route recommendation system by reducing the traffic jams and queuing time in POIs. Other like [20], the objective was to maximize the total scores in each POIs while maintaining the total travel time under constraints by GA. Wang et al [11] extend the Ant Colony Optimization algorithm by merging user interests with POI popularity and using crowd data to recommend trips. Chang et al [34] used a Greedy algorithm to minimize the process of trip planning and maximize user satisfaction with the best entertainment places while traveling to the destination.…”

Section: Related Workmentioning

confidence: 99%

“…The itinerary planning problems have been proved to be NP-hard and challenging. This is why the evolution approaches such as Genetic Algorithms (GA) have received increasing attention in this area recently [16]- [20]. The previous works on the itinerary recommendations use GA to solve such search and optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Yochum

Chang

et al. 2020

IEEE Access

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Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, tourists have different kinds of preferences about their itineraries, limited time budgets, unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that planning an itinerary is quite tedious, time-consuming, and challenging for them. In this paper, we propose an adaptive genetic algorithm for personalized itinerary planning for travelers to plan their itineraries better. Firstly, desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports) are considered in our approach. Secondly, we also take some general factors into account that travelers would consider in their preferences of an itinerary, which are mandatory POIs, the total number of POIs, the overall POI popularity, the overall cost, and the overall rating. Thirdly, we view this kind of recommendation task as a Multi-Objective Optimization problem, and we propose an adaptive genetic algorithm with the crossover and mutation probabilities (AGAM) for solving this problem to better find the best global solution. Fourthly, we allocate different weights to every factor which considered in our paper to generate a personalized itinerary recommendation for better meet many kinds of preferences of tourists. Finally, we compare our approach against baselines on real-world datasets which include six touristic cities, and the experimental results show that the AGAM achieves better recommendation performance in terms of the mandatory POIs, total POI visits, overall POI popularity, total travel time (including travel time and visit duration), overall cost, and overall rating.

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A Genetic Algorithm for Generating Travel Itinerary Recommendation with Restaurant Selection

Cited by 15 publications

References 9 publications

Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers

Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers

The The Efficiency of Hybridised Genetic Algorithm and Ant Colony Optimisation (HGA-ACO) in a Restaurant Recommendation System

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

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