Recommender Systems: Approaches, Challenges and Applications (Volume II)
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 22873
Special Issue Editors
Interests: artificial intelligence; group decision support systems; argumentation-based dialogues; affective computing
Special Issues, Collections and Topics in MDPI journals
Interests: recommender systems; group recommender systems; affective computing
Special Issues, Collections and Topics in MDPI journals
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.
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 system.
Dr. João Carneiro
Dr. Patrícia Alves
Dr. Goreti Marreiros
Guest Editors
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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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