Innovative Data Mining Techniques for Advanced 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 May 2025 | Viewed by 896
Special Issue Editors
Interests: recommender systems; social computing
Interests: social network analysis and mining; data-driven machine learning; crowdsourcing data processing; recommender systems and big data processing; multimedia database and streams
Interests: privacy-preserving machine learning; federated learning; trustworthy machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the digital landscape evolves, the demand for personalized user experiences has driven significant advancements in recommender systems. This Special Issue focuses on innovative data mining techniques that enhance the efficacy and accuracy of these systems across various applications, including e-commerce, content streaming, and social media. We invite contributions that explore methodologies such as collaborative filtering, content-based filtering, and hybrid approaches, alongside emerging techniques like explainable recommendation, cross-domain recommendation, and large language model-based recommendation. Additionally, we encourage authors to address challenges related to fairness in recommendation, bias in recommender systems, and the trustworthiness of recommendations. We also welcome studies on causal recommendation and contextualized recommendation. Case studies showcasing real-world applications and empirical evaluations of proposed methods are highly encouraged. By fostering a comprehensive understanding of these critical aspects, this Special Issue aims to contribute valuable insights and pave the way for future research in the rapidly evolving field of artificial intelligence.
Dr. Yue Kou
Dr. Xiangmin Zhou
Dr. Chaochao Chen
Guest Editors
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Keywords
- explainable recommendation
- cross-domain recommendation
- fairness in recommendation
- bias in recommender systems
- trustworthiness of recommendation
- causal recommendation
- contextualized recommendation
- large language model-based recommendation
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