Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
Introduced in 2020, the ACM RecSys Conference announced for the third time a ‘Reproducibility’ track at the ACM RecSys Conference Series. The call for papers (CfP) was published two days ago.
RecSys strongly encourages the submission of algorithmic papers that repeat and analyze prior work. We distinguish between:
* replicability papers, which repeat prior experiments using the original source code and datasets to show how, why, and when the methods work (or not); and
* reproducibility papers, which repeat prior experiments, either using the original code or using new implementations, in new contexts (e.g., different application domains and datasets, different evaluation methodologies and metrics) to further generalize and validate (or not) previous work.
Submissions regarding replicability or reproducibility papers are welcome in all areas related to recommender systems (see the main track for the list of topics). In both replicability and reproducibility papers, we expect authors to provide all materials required for repeating the tests performed, including code, data, and clear instructions on how to run the experiments. Submissions from the same authors of the reproduced experiments will not be accepted. Failure to provide all materials will result in desk rejection. Each accepted paper will be included in the conference proceedings and presented in a plenary session as part of the main conference program. We encourage authors to create a companion website for each paper where details on how the code and data can be reproduced or re-used.
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).