Sensor-Based Recommender System for Smart Education and Smart Living
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 531
Special Issue Editors
Interests: self-regulated learning; knowledge services, cloud computing; information security; smart living
Interests: smart education; knowledge graph; recommendation system; head pose estimation; facial expression recognization; human pose estimation; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are delighted to invite paper submissions for the Special Issue “Sensor-Based Recommender System for Smart Education and Smart Living”. The details can be found below.
In recent years, recommendation systems have gradually become a research hotspot and have been applied in many fields, such as movies, music, news, e-commerce, online learning systems, etc. The possibility of making personalized suggestions enhances the effectiveness of the recommendation system. Therefore, considering user preferences, personalities, and expectations can improve the quality of recommendations. The learning resource adaptation algorithm is the core module of personalized learning systems. The kind of recommendation algorithm for learners directly determines the ability of an adaptive learning system to provide intelligent and personalized services.
In order to improve accuracy, several recommendation technologies are proposed, which not only consider scoring information, but also auxiliary information, such as learner review data, social networks, and knowledge maps of learning resources, as well as joint matrix decomposition to make up for the shortcomings of scoring data and enhance the effectiveness of recommendation.
We invite authors to submit original research, new developments, experimental works, and surveys with the fields of new AI pedagogical solutions to overcome the challenges faced by recommendation systems and learning resource adaptation and improve the quality of recommendations. The topics of interest of this Special Issue include but are not limited to:
- Cross-cutting recommendations;
- Context-aware recommendation system;
- Personalized learning;
- Suggestions based on machine learning/deep learning;
- Novelty, diversity. Or contingency of learning resource adaptation;
- The interpretation method of learning resource adaptation;
- Cognitive and emotional aspects of learning resource adaptation (emotion, personality, emotion, motivation, etc.);
- Recommendation algorithm using context information and/or social network information/or knowledge maps;
- Learning resource adaptation privacy protection technology.
Prof. Dr. Zhaoli Zhang
Dr. Hai Liu
Dr. Sanjay Kumar Singh
Guest Editors
Manuscript Submission Information
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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 2600 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.
Keywords
- recommender systems
- personalized learning
- learning resource adaptation
- machine learning
- deep learning
- online education
- artificial intelligence
- intelligent recommendation
- collaborative filtering
- content-based filtering
- hybrid recommender systems
