Finally, the recommender systems community has its own journal: “Frontiers in Big Data | Recommender Systems”
Almost a year ago, we asked the question of why there was no journal on recommender systems? Now, there is a journal, and we applaud Bart Goethals (University of Antwerp) for initiating it. The journal “Frontiers in Big Data | Recommender Systems” is open access and welcomes all kinds of articles relating to recommender systems.
The scope is as follows:
With this special section of Frontiers in Big Data, we want to provide a dedicated and high quality resource collecting new research results, systems and techniques in the broad field of recommender systems. As one of the most popular big data applications, recommender systems are becoming more ubiquitous in our every day lives, being used in search engines, online retail, news, entertainment, travel, social networks, and much more. The research area is evolving and growing rapidly, but many challenges still remain. Apart from creating better recommenders, we face important challenges such as that of privacy, bubbles, evaluation, both online and offline, scalability, understanding both the human and economic impact, and much more.
This journal solicits original articles in both the research and practice of recommender systems, surveys and tutorials of important areas and techniques, and case studies of real-world implementations.
In addition, innovative interdisciplinary approaches that address combinations of economic, psychological, and computer science techniques are highly encouraged.
Reproducibility is a major concern within our research field, and it is the goal of this journal to guarantee that all contributions are valuable and shown to be reproducible.
https://www.frontiersin.org/journals/big-data/sections/recommender-systems#about
The editorial board includes some well-known names from the recommender systems community.
A dedicated journal is a big step for the recommender-systems community, and I hope the new journal will attract many high-quality articles. Besides the fact that this journal is a great initiative, I must, however, mention the controversy around the “Frontiers in…” journal series but I hope that this will not affect the willingness of authors to submit to the journal.
Disclaimer: I am a reviewer for the journal.
The “ACM Transactions on Recommender Systems” (ACM TORS) is revealed during the ACM RecSys Conference 2021
Call for Papers: Special Issue on Data Science for Next-Generation Recommender Systems
CfP (ACM TOIS): Special Section on Trustworthy Recommendation and Search
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).