A decision tree based recommendation system for tourists

Thiengburanathum, Pree; Cang, Shuang; Yu, Hongnian

doi:10.1109/iconac.2015.7313958

Cited by 16 publications

(6 citation statements)

References 18 publications

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“…The various classification and clustering algorithms are usually combined with the similarity distance results to develop the recommendation model. The support vector machine, decision trees, random forests, or even deep learning algorithms, like long short-term memory [26] are examples of traditional classification algorithms [25]. There are also many data clustering algorithms, but the most commonly used are Kmeans, hierarchical clustering, and K-nearest neighbour [27].…”

Section: Related Workmentioning

confidence: 99%

Travel Direction Recommendation Model Based on Photos of User Social Network Profile

Stefanovič

Ramanauskaitė

2023

IEEE Access

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Travelling is one of the most enjoyable activities for people of all ages. It is constantly looking for innovative solutions on how to tailor travel recommendations to the needs of its customers. The purpose of our proposed recommendation model is to suggest travelling countries based on photos from the user's social network account and metadata associated with the photos. Such recommendation models are highly dependent on the data used in the model preparation steps and on the technologies and methods implemented in the model. The newly collected data from the Instagram users' accounts were used in the model preparation. The recommendation system is based on the combination of four methods: object detection, similarity measures, classification, and data clustering. The novelty of the proposed recommendation model is that it adopts different data (Instagram photos) for travel direction recommendation, defines a new combined method, integrates results of similarity measurement and SOM application results into one final recommendation, and estimates the parameter impact for different components of recommendation model. A proposed evaluation measure has been used to conclude the results of the recommendation model and as a result the names of the travelling countries have been recommended. The results of the proposed recommendation model are promising, and the validation results demonstrate that on average 63% of the users who visited countries match the recommendations provided for the trip directions, while the accuracy of recommendations, matching user visited countries, but not presented in the photos for recommendation estimation, on average was 96%. The accuracy performance is very positive, while the recommendation system is fully automated and machine learning based. With time, the accuracy of the model may even increase by adopting the photo metadata (location).INDEX TERMS classification, object detection, recommendation model, self-organizing maps, similarity measure, photo of social networks.

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Section: Related Workmentioning

confidence: 99%

Travel Direction Recommendation Model Based on Photos of User Social Network Profile

Stefanovič

Ramanauskaitė

2023

IEEE Access

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“…Decision tree (DT) is a decisionmaking method easy to use, implement, and provides good results. It was widely used in several fields such as medicine [14], traffic control [15], and tourism [16]. In addition, it was used also as a decision-making tool for MG energy management.…”

Section: Introductionmentioning

confidence: 99%

A novel decision-making approach based on a decision tree for micro-grid energy management

Mansoury

Bourakadi

Yahyaouy

et al. 2023

IJEECS

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Environmental challenges such as climate change have accelerated humanity's need for renewable alternative energy sources. For this reason, we propose in this paper a decision-making strategy that allows controlling the flows of energy into a micro-grid (MG) compound of solar energy, batteries, and diesel generator (DG), and connected to the distributed network (DN). Therefore, the power supply to the loads is obtained either from the energy produced by solar sources, from the batteries, from the DN, or from the DG when renewable energy (RE) and batteries are depleted. To make the final decision, we consider four parameters at the same time: the energy produced by solar energy, the requested load, the state of charge of batteries (SoC), and the purchase or sale price. Decision tree (DT) is used to build the energy management strategy to ensure the availability of power on demand by making logical decisions about charging batteries, discharging batteries, buying necessary energy from DN, selling excess energy to DN, and recovering necessary energy from DG. The suggested DT approach is applied to a real MG to minimize the cost-benefit balance, and the comparison analysis demonstrates good results when compared to related works.

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“…With the ease of getting information and recommendations, tourists International Journal of Intelligent Engineering and Systems, Vol. 15 can easily plan their travels [5]. The aftereffect is that it can trigger them to return and promote the destinations they have visited to other potential tourists.…”

Section: Introductionmentioning

confidence: 99%

“…A game needs to get additional support from the recommendation system to provide the benefits of knowledge to its players [14]. With the development of a recommendation system, tourists can choose, compare and make decisions quickly [15]. In this study's case of tourism games, the main problem is how to produce recommendations for selecting halal tourist destinations for potential tourists.…”

Section: Introductionmentioning

confidence: 99%

Destinations Ratings Based Multi-Criteria Recommender System for Indonesian Halal Tourism Game

2022

IJIES

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Halal tourism is one of the tourism products that have the prospect of contributing to economic growth in Indonesia. Therefore, the government needs to increase promotions to increase tourist interest in halal tourism destinations in Indonesia. Game is one of the alternative promotional media that can also function as an educational medium for choosing halal tourism that is fun for potential tourists. This study proposes a recommendation system to support knowledge sources in the Indonesian halal tourism game. We use destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists. This method improves the ability of the conventional tourism recommendation system, which is generally based on a single criterion. In this study, we use eight destinations rating criteria as a reference for calculating the recommender system in the halal tourism game. Each of these criteria is a reference for tourists' assessment of halal tourist destinations in Indonesia. Next, we integrate the cosine-based similarity technique in MCRS to measure the level of similarity between players and previous tourist data sets. This research's testing phase uses the theme of halal tourism destinations in the Batu City area. The test results show that the number and composition of the tourism destinations item rating as input of the recommender system affect the accuracy, precision, recall, and F1 scores. Based on 40 experiments with different tourism destination item rating input configurations, it shows that the average value of accuracy = 0.60, precision = 0.67, recall = 0.64 and F1 score = 0.65.

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A decision tree based recommendation system for tourists

Cited by 16 publications

References 18 publications

Travel Direction Recommendation Model Based on Photos of User Social Network Profile

Travel Direction Recommendation Model Based on Photos of User Social Network Profile

A novel decision-making approach based on a decision tree for micro-grid energy management

Destinations Ratings Based Multi-Criteria Recommender System for Indonesian Halal Tourism Game

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