This special issue solicits the latest and significant contributions on developing and applying data science and advanced analytics for building next-generation recommender systems, and particularly on data+model-driven intelligent and personalized recommender systems.

Topics of Interest:

The special issue invites submissions on all topics of data science for recommender systems, including but not limited to:

Advanced data mining, machine learning and deep learning for recommender systems;
Automated recommender systems with automated model selection and parameter tuning in open and dynamic environment;
Big data analytics and its applications to recommender systems;
Context-aware and domain-driven recommender systems;
Data science theories and techniques for recommender systems;
Data-driven behavior modelling, analysis, and prediction for dynamic, session-based, sequential and next-best recommendation;
Non-IID recommender systems with complex couplings, interactions, relations and heterogeneities;
Recommender systems in low-quality large or small data and with misinformation; Personalized recommender systems and precision recommendation;
Recommender systems for light-weighted and energy-efficient devices, IoT, PDA and relevant contexts; and
Surveys, reviews and prospects on data-driven next-generation recommender systems.
Guest Editors:

Yan Wang (yan.wang@mq.edu.au), Macquarie University, Australia

Shoujin Wang (shoujin.wang@mq.edu.au), Macquarie University, Australia

Fikret Sivrikaya (fikret.sivrikaya@gt-arc.com), GT-ARC gGmbH, Berlin, Germany

Sahin Albayrak (sahin.albayrak@dai-labor.de), Technische Universität Berlin, Germany

Vito Walter Anelli (vitowalter.anelli@poliba.it), Polytechnic University of Bari

Important Dates:

Paper submission: extended to December 31, 2021
Submission Guidelines:
Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. All manuscripts must be prepared according to the journal publication guidelines and author’s instructions which can be found on the website (http://www.springer.com/41060). Papers will be reviewed following the journal standard review process. Please remember to select this special issue when you submit your manuscript in the submission system.

Enquiries:

Enquiries about this special issue can be sent to any guest editors.