Recommender Systems: Approaches, Challenges and Applications (Volume III)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 215

Special Issue Editors


E-Mail Website
Guest Editor
GECAD, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Interests: recommender systems; group recommender systems; affective computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
GECAD, Institute of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: artificial intelligence; multiagent systems; emotional agents; persuasive argumentation; group decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recommender systems have been applied in several domains (e.g., tourism, health, education, e-commerce, etc.) to help users determine more satisfactory choices. The possibility of formulating personalized recommendations enhances the effectiveness of recommender systems. As such, considering aspects such as user preferences, personality and expectations can improve the quality of recommendations. It is important to study and develop new intelligent strategies allowing a greater awareness of the user or group of users, while considering new ways of evaluating recommendation systems, such as diversity, satisfaction, user experience, coverage, trust, fairness and transparency.

Following the success of Volume I and Volume II of this Special Issue, in Volume III, we continue to assist all those interested in the topic to promote their vision and ideas.

The purpose of this Special Issue is to explore novel artificial intelligence solutions for overcoming the current challenges of recommender systems and to improve the quality of recommendations.

Topics relevant for this Special Issue include:

  • Group recommender systems;
  • Cross-domain recommendations;
  • Context-aware recommender systems;
  • Personalized recommendations;
  • Recommendations based on machine learning/deep learning;
  • Novelty, diversity or serendipity in recommender systems;
  • Explanation methods for recommender systems;
  • Cognitive and affective aspects in recommender systems (emotions, personality, mood, motivations, etc.);
  • Transfer learning in recommender systems.

Dr. Patrícia Alves
Prof. Dr. Goreti Marreiros
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 submissions that pass pre-check are 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. Electronics 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 2400 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
  • group recommender systems
  • cold-start problem
  • collaborative filtering
  • content-based filtering
  • hybrid recommender systems
  • machine learning
  • deep learning
  • reinforcement learning for recommender systems
  • affective computing in recommender systems

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Published Papers

This special issue is now open for submission.
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