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Special Issue "Challenges and New Opportunities for Next-Generation Recommender Systems"
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".
Deadline for manuscript submissions: 15 October 2023 | Viewed by 119
Special Issue Editors
Interests: recommender systems; natural language processing; multi-modal data analysis
Interests: recommender systems; data mining; personalized user modeling; streaming data analysis; graph learning-based recommendation; natural language processing for recommendations
Interests: recommender systems; user modeling; technology-enhanced learning; FinTech
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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 2000 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