AI and Machine Learning in Recommender Systems and Customer Behavior
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 1052
Special Issue Editors
Interests: deep learning applications; business analytics; personalized services
Interests: recommender systems; big data analytics; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) and machine learning (ML) into recommender systems has significantly transformed the provision of personalized services. Nevertheless, understanding customer behavior remains a crucial aspect that underlies the efficacy of these systems. This Special Issue seeks to investigate technological advancements in AI-/ML-driven recommendation technologies and the theoretical foundations of customer behavior analysis that inform and guide these innovations.
In recent years, the exponential growth of digital data has necessitated more sophisticated algorithms to process and analyze user interactions, preferences, and behaviors. While AI and ML play a critical role in optimizing these processes, understanding the cognitive and behavioral patterns of customers is equally important for developing effective customer-centered recommender systems. Thus, we invite empirical and theoretical research addressing these dual aspects: the development of advanced recommender system technologies and the underlying behavioral theories that enhance personalization strategies.
Topics of interest include, but are not limited to, the following:
- AI and ML techniques for recommender systems;
- Theoretical frameworks for understanding and predicting customer behavior;
- Behavioral models in personalized service;
- Multimodal data integration for enhanced recommendation accuracy;
- Solutions for data sparsity and cold-start problems in recommender systems;
- Transfer learning, domain adaptation, and personalization in recommendations;
- Ethical issues, fairness, and bias in AI-driven personalization;
- Case studies and applications of recommender systems across industries.
Dr. Qinglong Li
Prof. Dr. Jae Kyeong Kim
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning applications in personalization
- deep learning techniques in personalization
- recommender systems in business contexts
- predictive analytics in personalization
- predicting user behavior in personalization
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