Special Issue "Affective Recommender Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2022.

Special Issue Editors

Dr. Yong Zheng
E-Mail Website
Guest Editor
College of Computing, Illinois Institute of Technology, Chicago, IL, USA
Interests: recommender systems; user modeling; web intelligence
Dr. María N. Moreno García
E-Mail Website
Guest Editor
Department of Computer Science and Automatics, University of Salamanca, Salamanca, Spain
Interests: machine learning; web mining; recommender systems; social media mining
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Special Issue Information

Dear Colleagues,

Emotional states can play an important role in the process of decision making. Researchers have demonstrated the impact of emotions on the effectiveness of recommender systems. Affective recommender systems (ARS) or emotion-aware recommender systems (EARS) are usually associated with multidisciplinary research, including artificial intelligence, human factors, mood or emotions, facial expressions, and physiological information with human–computer interaction.

The development of affective recommender systems promotes various research topics, such as user interaction and interfaces, algorithm design and evaluations, computational efficiency, deep learning-based recommendation models, and recommendation explanations. This Special Issue on “Affective Recommender Systems” aims to promote new theoretical models, approaches, algorithms, and applications related to ARS. Possible topics include but are not limited to:

  • Novel and effective models and algorithms for ARS/EARS;
  • New approaches to utilize emotions in recommender systems;
  • Review mining or sentimental analysis to assist ARS/EARS;
  • User-centric studies and evaluations in ARS/EARS;
  • Recommendation explanations in ARS/EARS;
  • Novel applications in ARS/EARS;
  • Emotion detection or recognition in recommender systems;
  • Emotion representation or representation learning in recommender systems;
  • Novel paradigms and theoretical foundations in ARS/EARS;
  • Preference elicitation in ARS/EARS;
  • User interface design and user-adaptive interaction in ARS/EARS.

Dr. Yong Zheng
Dr. María N. Moreno García
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. Applied Sciences 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 2300 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

  • affective computing
  • recommender systems
  • affective recommender systems
  • emotion
  • emotion-aware
  • emotion-aware recommender systems
  • human factors

Published Papers

This special issue is now open for submission.
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