Volume 1, Issue 1 of ACM TORS published
Congratulations are in place: A few days ago, volume 1, issue 1 of the ACM Transactions on Recommender Systems was published. The issue consists of an editorial by the editors-in-chief Li Chen, and Dietmar Jannach, and three research articles. Visit https://dl.acm.org/toc/tors/2023/1/1 for more details.
The ACM Transactions on Recommender Systems (ACM TORS) is a scholarly journal dedicated to the publication of high-quality research in the field of recommendation systems. These systems are a crucial component of modern technology and play an important role in personalized information access, e-commerce, and social networking.
ACM TORS is published by the Association for Computing Machinery (ACM), one of the largest professional organizations in the field of computer science. The journal aims to provide a forum for the exchange of new ideas, theories, techniques, and applications in the field of recommendation systems. ACM TORS is well respected and considered to be a leading publication in the area.
The scope of ACM TORS includes all aspects of recommendation systems, including but not limited to, algorithms, user modeling, evaluation, interfaces, and scalability. The journal welcomes both theoretical and empirical contributions and encourages submissions that provide novel insights and ideas, as well as those that demonstrate the practical benefits and limitations of recommendation systems.
ACM TORS has a rigorous peer-review process to ensure the quality and validity of the published research. The submissions are evaluated by a team of experts in the field who assess the quality, originality, and significance of the work. The authors of accepted papers receive constructive feedback from the reviewers to help improve the quality of their work.
CfP (ACM TOIS): Special Section on Trustworthy Recommendation and Search
Finally, the recommender systems community has its own journal: “Frontiers in Big Data | Recommender Systems”
Call for Papers: Special Issue on Data Science for Next-Generation 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).