ACM Transactions on Recommender Systems Shifts Social Media Focus to LinkedIn
The ACM Transactions on Recommender Systems (ACM TORS) journal has announced a strategic shift in its social media presence — moving its primary communication channel from X (formerly Twitter) to LinkedIn.
This decision is driven by practical considerations. Over the past months, many members of the recommender systems (RecSys) research community have become less active on X. To maintain effective engagement, ACM TORS aims to share news and updates where its audience is most active — currently, that means LinkedIn.
While the journal acknowledges the strengths of X — particularly its concise format that suited academic announcements — it has observed declining interaction on the platform. For now, ACM TORS will keep its X account open but expects LinkedIn to host the most comprehensive and up-to-date content. The team will evaluate over time whether maintaining both channels continues to make sense.
In the near future, all major announcements, calls for papers, and publication updates will appear first and most completely on LinkedIn. The journal encourages the community to follow and engage through the following channels:
- LinkedIn page: https://www.linkedin.com/company/acm-tors/
- Hashtags: #ACM_TORS and #RecSys on both X and LinkedIn
- Official website: https://tors.acm.org
- Recommender-Systems.com Blog: https://recommender-systems.com/news/category/acm-tors-news/
The editorial team looks forward to continued discussion and exchange within the recommender systems research community — now primarily through LinkedIn.
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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).