Call for Papers: Special Issue on Data Science for Next-Generation Recommender Systems
The International Journal of Data Science and Analytics released a call for papers for a special issue on recommender systems. The deadline is June 30th, 2022.
We are living in the age of data, where nearly every task we conduct in our daily lives depends on data and can be tracked and supported digitally. Massive data of different types, including numeric variables, images, videos, music, text, etc., could be collected when shopping, working, socializing, communicating, relaxing and traveling, as part of our daily lives. As a multi-disciplinary field that integrates mathematics, statistics and computer science, data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, with the ultimate goal to support decision making. In this context, recommender systems have been one of the most important applications of data science. Recommender systems use advanced analytics and learning techniques to select relevant and significant information from massive data and inform users’ smart decision-making on their daily needs.
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.
Guest Editors are:
- Yan Wang, Macquarie University, Australia
- Shoujin Wang, Macquarie University, Australia
- Fikret Sivrikaya, GT-ARC gGmbH, Berlin, Germany
- Sahin Albayrak, Technische Universität Berlin, Germany
- Vito Walter Anelli, Polytechnic University of Bari
For more call for papers relating to recommender systems, please refer to https://recommender-systems.com/news/category/community/call-for-papers-cfp/
The ‘ACM TORS’ journal had a successful first-year
Special Issue CfP: “Perspectives on Recommender Systems Evaluation” in ACM TORS
CfP ACM TOIS: Special Section on Causality Representation Learning in LLMs-Driven Recommender Systems
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).