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Special Issue "Sensor-Based, Context-Aware Recommender Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 May 2020).

Special Issue Editors

Prof. Dr. Olga C. Santos
E-Mail Website
Guest Editor
aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, 28040 Madrid, Spain
Interests: recommender systems; affective computing; user modelling; human computer interaction; personalized interaction; human activity recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jesús G. Boticario
E-Mail Website
Guest Editor
aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, 28040 Madrid, Spain
Interests: artificial intelligence; human computer interaction; user modelling; adaptive systems in education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recommender systems use their intelligence to support users in decision-making processes. Their power can increase when information from the user and the context is sensed. The user information can be obtained from wearables so that the user’s emotions, actions performed, location, physical activity, etc., can be taken into account. The context information comes from context-aware sensors where intelligent features can be controlled. In this Special Issue we want to deepen the information that can be collected from sensors to enrich the recommendation process so that the context and the user are really taken into account. In particular, we are interested in comparing existing sensors and identifying if they provide the required information both from the user and the context, or whether new sensors need to be developed to make recommender systems more powerful. In doing so, there is an interplay between the sensor-based recommendation and the user reaction that opens many unresolved questions regarding when and how this should be managed and to what extent this impacts on the user’s decision-making process. In addition, we are also interested in techniques for processing the sensor data collected and the indicators obtained, including how to access them both from the user and the system viewpoints. Finally, new applications for recommender systems that take advantage of this contextual information are welcome.

  • Recommendation possibilities with sensor data obtained from commercial wearables.
  • Custom-made smart sensors and wearables for new recommendation opportunities.
  • Context-aware sensors that can expand the features to be controlled in an ambient intelligent setting.
  • Artificial intelligence techniques to process sensor data for recommender systems.
  • Intelligent management of the interplay between sensor-based recommendations and the user.
  • Goal and quality of indicators obtained from sensor data that can drive the recommendation process.
  • Intelligent multimodal delivery of recommendations through diverse sensorial channels.
  • User-friendly graphic tools to follow-up the sensor-based context-aware recommendations delivered.
  • Application of sensor-based context-aware recommender systems (educational scenarios, affective support, physical activity training, etc.).

Prof. Dr. Olga C. Santos
Prof. Dr. Jesús G. Boticario
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Article
Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System
Sensors 2020, 20(12), 3597; https://doi.org/10.3390/s20123597 - 25 Jun 2020
Cited by 2 | Viewed by 914
Abstract
Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To [...] Read more.
Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system. Full article
(This article belongs to the Special Issue Sensor-Based, Context-Aware Recommender Systems)
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