A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques

Torres-Ruiz, Miguel; Mata, Félix; Zagal, Roberto; Guzmán, Giovanni; Quintero, Rolando; Moreno-Ibarra, Marco

doi:10.1007/s10055-018-0366-z

Cited by 40 publications

(16 citation statements)

References 31 publications

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“…Chatzidimitris et al [33] proposed a collaborative filtering-based mobile CARS, which has been coordinated in a brilliant retailing stage that empowers area based quest for retail items and administrations. Ruiz et al [34] proposed a hybrid approach to make recommendations for museum visits.…”

Section: Related Workmentioning

confidence: 99%

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Maheswari

Geetha

Kumar³

et al. 2021

IEEE Access

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Traditional recommendation approaches for the mobile apps basically depend on the app related features. Now a days many users are in quench of app recommendation based on the version description. In the proposed system the hybrid app recommending framework is introduced which is considering the version of the mobile apps. This novel framework named "Probabilistic Evolution based Version Recommendation Model (PEVRM)" integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance.

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Section: Related Workmentioning

confidence: 99%

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Maheswari

Geetha

Kumar³

et al. 2021

IEEE Access

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“…This research was exclusively limited to one type of tourist place. Torres-Ruiz et al [7] proposed a touristic recommender system that semantically recommended itineraries using social network data, which included Facebook, Foursquare, and Twitter. In this research, the typical questions were classified and the answers were displayed using a mobile AR application.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sci. 2021, 11, 6047 2 of 25 field [4,5], data modeling [2], and big data [6,7] integration, and personalizing the information visualization according to the user preferences [8,9]. With the personalization process of developed AR systems, the user preferences were extracted by either the user inputs or by mining the user history.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

et al. 2021

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A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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“…How to tour it and get the best experience? A hybrid approach to make recommendations for museum visits is proposed by [14], including Internet of Things architecture of beacons, data mining and machine learning. The result is a customized tour with augmented reality that contains a set of recommendations of how to visit a set of museums and obtain a better experience of the visit whose prototype is available in Google Play and named "Historic Center".…”

Section: Related Workmentioning

confidence: 99%

Phobia Treatment with Face-Your-Fear VR App

Loucif¹,

Negreiros²,

Stifi³

et al. 2021

JCC

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Technology plays an important role in people's daily life, for they depend on it to perform many tasks. Among the prominent technologies are smartphones and Virtual Reality (VR). Developing mobile apps and integrating VR technology allow users to experience an artificial environment as if it is real such as the simulation of phobias. This research paper proposes a mobile application named Face-Your-Fear (FyF) integrating VR to help any user to live a cybernetic experience, overcome the illness, know about phobia symptoms, and get extra information on two types of phobia: aquaphobia and achluophobia. Its architecture, features, development, interfaces and limitations are presented, including the main results of the qualitative survey to users.

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A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques

Cited by 40 publications

References 31 publications

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

Phobia Treatment with Face-Your-Fear VR App

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