Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach

Arif, Yunifa Miftachul; Putra, Duvan Deswantara; Wardani, Dyah Wulan Sumekar Rengganis; Nugroho, Supeno Mardi Susiki; Hariadi, Mochamad

doi:10.1016/j.heliyon.2023.e14267

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…This study's results indicate that using games as a medium for learning mathematics positively influences learning activities and understanding of mathematics [21]. Based on research by Sun et al it is necessary to increase adaptive intelligence to the surrounding environment, according to Yunifa et al, one of the adaptive intelligence technologies is ambient intelligence which can be applied to serious games to arrange response scenarios for selecting tourist destinations [22]. In this study using ambient intelligence to set scenarios for selecting responses to games for selecting mathematics learning materials.…”

Section: Related Workmentioning

confidence: 83%

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

Nurhayati¹,

Arif²

2023

IJACSA

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The challenge to increasing understanding of mathematics lessons for students in madrasah schools makes the learning process require the support of adaptive alternative learning media. In this study, we propose a serious game-based learning media supported by virtual reality and ambient intelligence technology to equip students with adaptive responses to subject matter scenarios. Ambient intelligence works based on recommendations generated by the Multi-Criteria Recommender System (MCRS). In calculating a similarity between users and reference data, MCRS uses cosine-based similarity calculations, and average similarity is used for ranking. We developed this learning media experiment called Math-VR using the Unity game engine. The experimental test results show that MCRS-based ambient intelligence technology can provide an adaptive response to the choice of geometry subject matter recommendations for students according to their pre-test results. The analysis results show that the recommendation system as part of ambient intelligence has the highest accuracy rate of 0.92 when using 80 reference data.

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Section: Related Workmentioning

confidence: 83%

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

Nurhayati¹,

Arif²

2023

IJACSA

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“…In this section, related recommender systems are discussed. Recommender systems have been proposed and applied by researchers covering diverse areas including domestic energy efficiency, industrial automation, fairness measurement, companies' internal recruitment, guests' hospitality, replicable knowledge awareness, tourists' destinations' selection, pervasive data streaming, public complaints and e-commerce [5][6][7][8][9][10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Social Filtering Model for Event Recommendation Services

Oyemade,

Aworonye

2024

AJAI

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In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

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“…The method provides direction on how to increase the favorable views of inhabitants. Arif et al [ 23 ] proposed a decentralized recommender system utilizing known and unknown rating approaches for the ambient intelligence of tourism places. The method generates recommendations for choosing tourist locations as a guide for choosing scenario visualizations.…”

Section: Related Workmentioning

confidence: 99%

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values

Lou

2024

PLoS ONE

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Environmental, cultural, and public service-dependent factors encourage the development of a country’s tourism. In recent years, automated tourism development using statistical and accumulated data has been exploited to recommend attractive tourist features. This article thus discloses an intelligent development assessment method (IDAM) using cumulative factors (CFs) for deriving development-focused improvement in tourism. This method accounts for public services and environmental and cultural factors that promote tourism for better assessment. The fuzzy process identifies the maximum possible impacting factors by independently evaluating the reviewed values. Based on the reviewed values, the manipulation of factor relationships is derived to identify even trivial factors impacting development. The fuzzy outputs are thus integrated with optimistically impacting development factors to provide attractive recommendations. Such recommendations are analyzed using fuzzy data for previous and current development factors for new decisions. The system’s efficiency was evaluated using the recommendation ratio, ensuring a 48.58% success rate, a development rate of 0.105%, a 4-factor detection rate, and a review-based assessment rate of 55.5% for a sample size of 5,000 visitors.

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Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach

Cited by 12 publications

References 37 publications

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

Deep Learning Social Filtering Model for Event Recommendation Services

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values

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