Springer International Journal of Data Science and Analytics
Call for Papers: Special Issue on Data Science for Next-Generation Recommender Systems

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.

Submissions are invited 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.

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).

*** Please remember to select this special issue when you submit your manuscript in the submission system. ***

https://www.springer.com/journal/41060/updates/17193470