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Recommender systems 2021 : SN Computer Science Call for Papers: Topical Issue on Advanced Theories and Algorithms for Next-generation Recommender Systems

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Link: https://resource-cms.springernature.com/springer-cms/rest/v1/content/18491852/data/v6
 
When N/A
Where N/A
Submission Deadline Dec 31, 2020
Notification Due May 20, 2021
Final Version Due Jun 20, 2021
Categories    recommender systems   user modelling   machine learning   recommendations
 

Call For Papers

Recommender systems have become one of the most important and practical applications of artificial intelligence (AI). In the era of digital economy, recommender systems are becoming increasingly popular and have been planted in nearly every corner of our daily life including online shopping, route planning, precision health, etc. However, facing the complex and uncertain real-world scenarios, the existing recommender systems have shown their limitations in fulfilling the users’ requirements, such as the lack of robustness in handling noise data and attacks, and their inability to interact with users and to explain the recommendations. To this end, it is necessary to develop next-generation recommender systems, e.g., trustworthy, conversational and explainable recommender systems, by substantially taking the advantages of the powerful AI theories and techniques. On the one hand, next-generation recommender systems are not only more robust when facing the noisy data and shilling attacks, but are also more user-friendly by providing better interaction, conversation with the end-users as well as good explanations of the recommendation results; on the other hand, the deep learning dominated AI techniques have shown great potential in dealing with various kinds of complex data as well as modelling and predicting users’ complex behaviors. Naturally, AI-enabled next-generation recommender systems are one of the most promising directions in computer science.

This topical issue aims to collect 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 next-generation recommender systems, supported by advanced AI and machine learning techniques. Those theories and algorithms that focus on the particular issues in recommender systems, including interaction, preference elicitation, privacy, trust, accountability, emotions/personality etc. are particularly welcome.

• the tentative date of paper submission: 31 December 2021
• Submission Deadline: 2022 09 30

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