About this Research Topic
Collaborative filtering based recommender systems are facing new challenges in responding to the trends of data explosion, changing user preferences, frequent model updates, and the demands of personalized real-time dynamic recommendations. Therefore, there is an increasing focus on how to improve the accuracy, coverage, diversity, adaptability, and scalability of recommender systems, how to extract in-depth relationships among products and customers, and how to make users satisfied with the recommended items.
This Research Topic aims to collect state-of-the-art theories, models, and applications for collaborative filtering-based recommender systems, enabled by machine learning, data mining, and advanced analytics. The goal is to explore intelligent recommendation issues including:
- Intent and preference modeling
- Serendipitous recommendations
- Personalized recommendations
- Real-time recommendations
- Next-best recommendations
- Cross-domain recommendations
In a context-aware, dynamic, and user/product/domain-specific manner. This Research Topic will provide the academic and industrial communities with a collection of the most recent theoretical and practical advances in recommender systems, including cutting-edge theories, foundations, and learning systems as well as actionable tools and impactful case studies of intelligent recommendation, supported by collaborative filtering techniques.
Topics of particular interest include, but are not limited to:
- Models and algorithms to improve collaborative filtering based recommendation quality
- Novel collaborative filtering based recommender system applications
- Privacy-preserving techniques for collaborative filtering based recommender systems
- Multi-source information enhanced collaborative filtering based recommender systems
- Integrating advanced AI/ML/DL techniques with collaborative filtering for recommendation
- Cold-start/data sparsity issues in collaborative filtering based recommender system
Keywords: Recommender Systems, Collaborative Filtering, Information Fusion, Data Sparsity, Personalized Ranking
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.