Dear Colleagues,

Recommender systems rely on users' explicit or/and latent interests to offer personalized suggestions of items automatically, aiming to help users cope with information overload. Despite its indispensable role in the areas of e-commerce, targeted advertising, intelligent medical assistants and online media, recommender systems are still facing many challenges which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of recommender systems, multi-stakeholder recommendation, data sparsity issues as well as misinformation detection. Fortunately, with the advancement of deep learning, multi-modal data have become better accessible for AI technology. The great success of language modelling techniques also inspires recommendation models to alleviate the aforementioned challenges through knowledge transmission and distillation from the pre-trained models.

This special issue solicits the latest and significant contributions on developing and applying efficient user modeling and advanced machine learning techniques for building next-generation recommender systems and particularly on data and model-driven intelligent recommender systems.

Topics of interest include but are not limited to, the following:

• Cross-domain/source recommendation
• Context-aware/group/multi-criteria/multi-objective recommender systems
• Multi-modal recommendation
• Explainanality/transparency in recommender systems
• Fairness/bias in recommender systems
• Efficient and scalable recommendation techniques
• Pre-trained recommendation model
• Self-supervised learning for recommendation
• Data sparsity, cold start and long-tail issues in recommender systems
• Knowledge transfer and distillation in recommender systems
• Surveys, reviews and prospects on next-generation recommender systems

Dr. Peng Liu
Dr. Lemei Zhang
Dr. Yong Zheng
Guest Editors

Manuscript Submission Information

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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.

• recommender systems
• collaborative filtering
• cross-domain recommendation
• multi-modality recommendation
• NLP for recommendation
• data sparsity and cold-start issue
• explainable recommender systems
• user modelling