Personalizing the Museum Experience through Context-Aware Recommendations

Benouaret, Idir; Lenné, Dominique

doi:10.1109/smc.2015.139

Cited by 40 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, finding relevant artworks to see in a limited amount of time is a crucial task. Such context-based recommender systems (Benouaret and Lenne 2015) for mobile devices adapt to the users profiles and is sensitive to their context (location, time, expertise, etc.). These recommenders improve the visitors' experience and help them build their tours on-site according to their preferences and constraints.…”

Section: Related Workmentioning

confidence: 99%

An overview of recommender systems in the internet of things

Felfernig¹,

Erdeniz²,

Uran³

et al. 2018

J Intell Inf Syst

View full text Add to dashboard Cite

The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

An overview of recommender systems in the internet of things

Felfernig¹,

Erdeniz²,

Uran³

et al. 2018

J Intell Inf Syst

View full text Add to dashboard Cite

show abstract

“…[ 61,82,94,114,119,131,134,137,165] Vector Information is gathered both implicitly and explicitly, which helps to reshape the relationship between users and contexts. [75,77,80,101,105,110,120,141,144,[165][166][167] …”

Section: Machine Learning Vectormentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

View full text Add to dashboard Cite

Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

show abstract

“…Considering applications focused on museum collections, [2] describes the importance of recommender systems because of the large amount of information displayed to the user when visiting a museum. Meanwhile, multiagent systems can be considered as the union of various intelligent entities coordinating actions and interacting in a specific environment trying to fulfill defined objectives to find an answer [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent System Used for Recommendation of Historical and Cultural Memories

Gélvez¹

2019

TECCIENCIA

View full text Add to dashboard Cite

In this document the proposal of a recommendation system based on multi agent is made allowing the analysis of user behavior when visiting historical and cultural memories, giving recommendations based on qualifications and duration times for the observation of art pieces. It is also possible to see the system architecture, the server used for the development of the multiagent system, as well as the communication between agents to carry out a route, and the functionality for recommending new routes to a user. The multi-agent system uses a neural network that allows to analyze the behavior of a user in a route; using the feedback given for the neural network the data is checked, allowing determine the user preferences. A set of historical and cultural memory data set is used to generate recommendations; in addition, a user storage API is employed. For the system visualization, this prototype is connected with an augmented reality application that allows users access to visit art pieces and use predefined preferences.

show abstract

Personalizing the Museum Experience through Context-Aware Recommendations

Cited by 40 publications

References 13 publications

An overview of recommender systems in the internet of things

An overview of recommender systems in the internet of things

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Multi-Agent System Used for Recommendation of Historical and Cultural Memories

Contact Info

Product

Resources

About