Improvement of Multi-Purpose Travel Route Recommendation System Based on Genetic Algorithm

Yuan, Chen; Uehara, Masato

doi:10.1109/candarw.2019.00060

Cited by 12 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the results of the search for the recommendation system in this literature review consist of 24 papers consisting of recommendation system papers, Hybrid, Collaborative Filtering, Content-Based Filtering, K-means, K-NN, utilization of social media as a recommendation system, TOPSIS, Search Engine Optimization (SEO), Dynamic Multimodal Route and Travel Recommendation System, Deep Learning, travel recommendations. Table 1 shows the system can be applied in any field with the context of recommending something, such as tourism, [4][20] [13], film [14]. Zhiyang Jia, et al [11] Collaborative Filtering and Cosine Build an online application capable of listing objects w data of personal preferences for tourists Lutfi Ambarwati and Baizal [12] Hybrid Collaborative Filtering and Knowledge-based Filtering and using the analysis of variance (ANOVA)…”

Section: Resultsmentioning

confidence: 99%

“…A recommendation system was built to determine tourist destinations according to user preferences by determining tourist destinations, costs, and categories of tourists ChenYuan, et al [13] Genetic Algorithms Utilizing genetic algorithms in the application of a travel recommendation system Jiang Zhang, et al [14] Collaborative Filtering and Kmeans Implementing a recommendation system using Collaborative Filtering and K-means to recommend Zhihua Cui [15] Collaborative Filtering and Kmeans [24] Collaborative Filtering and Association rule Build a recommendation system to help online sellers better meet customer needs and maintain their loyalty…”

Section: Resultsmentioning

confidence: 99%

“…Results can provide recommendations for tourist destinations according to user wishes by determining tourist destinations, costs, and the category. Other algorithms such as the Genetic Algorithm [13] can help in increasing the computational efficiency in a visit, for example, and can shorten the calculation time compared to standard algorithms.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Literature Review Recommendation System Using Hybrid Method (Collaborative Filtering & Content-Based Filtering) by Utilizing Social Media as Marketing

Praditya¹

2021

ComEngApp

View full text Add to dashboard Cite

The recommendation system is an application model based on observations of the circumstances and customer desires. In the recommendation system, several methods are used to support how the system works in producing information. One method of recommendation system that is quite popular is the method Hybrid. Several researchers have successfully applied this method in developing a tourism recommendation system, therefore to achieve the goal of implementing a tourism recommendation, it is better to take advantage of a marketing technique such as promotion in order to increase sales and attract more comprehensive customers. Therefore, a literature review on the method hybrid (Collaborative Filtering & Content-Based Filtering) of this travel recommendation system is carried out to collaborate between methods, algorithms, and a tool or media marketing applied in a recommendation system.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Literature Review Recommendation System Using Hybrid Method (Collaborative Filtering & Content-Based Filtering) by Utilizing Social Media as Marketing

Praditya¹

2021

ComEngApp

View full text Add to dashboard Cite

show abstract

“…In contrast to works based on the Orienteering problem, there are several optimization models like Heuristic optimization, Mathematical model, and other combinatorial optimization for solving multi-objective optimization problems. In [19], the objective function was minimized by an optimized GA during the search process while the high scores were reorganized in cross mutation phase. 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.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Yochum

Chang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Notable works that use MOGA for tour recommendation start by defining constraints relevant to tourists (e.g. time, distance, budget), and then they evaluate possible solutions from a list of Points of Interest (POIs) based on these constraints while ensuring solutions match tourist preferences [2,3,4]. Our proposed method extends our previous work [5], which recommends a set of POIs that match the user's preference considering their relatedness.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Genetic Algorithm for Optimal Route Selection from a Set of Recommended Touristic Activities

Orama

Moreno

Borrás

2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

It is a known fact that the order in which touristic activities are experienced plays a role in how enjoyable they are. This is the reason why tourists prefer to book carefully prepared day tours on arrival to a new destination, as they allow them to see the essence of the destination while traversing scenic routes. Tours are great, but they are expensive, do not allow room for personal exploration, and are built as a one-size-fits-all which does not consider the individual preferences of the tourist. In contrast, it is possible to make an optimal selection and ordering of touristic activities from a larger set of possibilities that match a tourist’s personal preferences, balancing important aspects like diversity, spatial proximity, or degree of interest on popular places. We propose a multi-objective genetic algorithm that uses a weighted averaging operator to balance four diverse objective functions crafted to maintain diversity, proximity, interest on popularity, and cultural preference. The system has been evaluated against four baseline algorithms and found to perform significantly better for the specified purpose.

show abstract

Improvement of Multi-Purpose Travel Route Recommendation System Based on Genetic Algorithm

Cited by 12 publications

References 3 publications

Literature Review Recommendation System Using Hybrid Method (Collaborative Filtering & Content-Based Filtering) by Utilizing Social Media as Marketing

Literature Review Recommendation System Using Hybrid Method (Collaborative Filtering & Content-Based Filtering) by Utilizing Social Media as Marketing

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Multi Objective Genetic Algorithm for Optimal Route Selection from a Set of Recommended Touristic Activities

Contact Info

Product

Resources

About