CfP ACM RecSys Workshops 2022
The ACM RecSys conference released its call for papers for workshops:
We are pleased to invite you to contribute to the 16th ACM Conference on Recommender Systems (RecSys 2022), the premier venue for research on the foundations and applications of recommendation technologies. The upcoming RecSys conference will be held from September 18th to September 23rd 2022. The conference will continue RecSys’ practice of connecting the research and practitioner communities to exchange ideas, frame problems, and share solutions. RecSys 2022 is pleased to invite proposals for workshops to be held in conjunction with the conference, which will be held in Seattle, USA, with both in-person and virtual workshops. The goal of the workshops is to provide additional venues for discussing novel ideas as well as recent results of research in progress. In 2022 our goal is to have a balanced workshop program which comprises workshop formats of different types and a combination of newly emerging, currently evolving, and historically understudied topics. Different full-day and half-day workshop formats are possible, for example: Read more…
https://recsys.acm.org/recsys22/call/#content-tab-1-2-tab
CfP (ACM TOIS): Special Section on Trustworthy Recommendation and Search
CfP ACM TOIS: Special Section on Causality Representation Learning in LLMs-Driven Recommender Systems
CfP for ACM TORS Special Issue: Knowledge Transferring for Recommender Systems (KT4Rec)
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).