From Good Intentions to Meaningful Impact: A Design Justice Approach for News Recommendation | ACM Transactions on Recommender Systems News recommender systems are increasingly positioned as tools for enhancing public discourse, yet their design often reflects narrow commercial or technological goals and overlooks systemic inequaliti...

The very first paper for my dissertation has just been accepted to #ACMTORS! If you enjoyed my feminist rants on technology, you might enjoy "From Good Intentions to Meaningful Impact: A Design Justice Approach for News Recommendation": dl.acm.org/doi/10.1145/...

@nadjaschaetz.bsky.social

So happy that our #ACMTORS journal paper on #GreenRecSys has been accepted just in time for @recsys.bsky.social / #recsys2025. This is the continuation of the work we presented at #recsys2024.

Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters | ACM Transactions on Recommender Systems In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources....

Just accepted to #ACMTORS: "Recommender Systems for Good (#RS4Good): Survey of Use Cases and a Call to Action for Research that Matters" - @dietmarjannach.bsky.social, myself, @recsysmare.bsky.social, and Markus Zanker dl.acm.org/doi/10.1145/... #recsys #recsys4good #ai4good

TORS EIC Selected 2024 Featured Articles ACM Transactions on Recommender Systems Co-Editors-in-Chief Li Chen, Hong Kong Baptist University, China Dietmar Jannach, University of Klagenfurt, Austria ACM Transactions on Recommender Systems (TORS) publishes high quality papers that address various aspects of recommender systems research, from algorithms to the user experience, to questions of the impact and value of such systems, on a quarterly basis. The journal takes a holistic view on the field and calls for contributions from different subfields of computer science and information systems, such as machine learning, data mining, information retrieval, web-based systems, data science and big data, and human-computer interaction. Moreover, interdisciplinary research works are welcome as well. Such works may either be based on insights from related fields, e.g., marketing or psychology, or apply recommendation technology in novel application areas. Recommender Systems TORS EICs are pleased to announce the following 2024 Featured Articles: Values into Recommender Systems: An Interdisciplinary Synthesis Where Are the Values? A Systematic Literature Review on News Recommender Systems Revisiting Bundle Recommendation for Intent-aware Product Bundling Towards a Causal Decision-Making Framework for Recommender Systems Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study

Excited to see our paper, "Where Are the Values? A Systematic Literature Review on News Recommender Systems" (doi.org/10.1145/3654...), selected as a #TORS EIC Featured Article for 2024!

Great collaboration with @kfvanes.bsky.social 🚀📚

#ACMTORS #RecSys #News #vales #EXDIGIT #PLUS #UniSbg

Still trying to figure out how to scratch your #recsys itch after this year's @recsys.bsky.social? Have a look at the #ACMTORS Call for papers for the Special Issue on #RecommenderSystems for Good. Will you be be submitting a paper as an xmas gift to the RecSys community?
dl.acm.org/pb-assets/st...

TORS: Vol 2, No 1

Extremely happy to see that our (Eva Zangerle , @christinebauer.bsky.social ) Special Issue on Perspectives on Recommender Systems Evaluation in the ACM Transactions on Recommender Systems is now available on ACM DL dl.acm.org/toc/tors/202...
#recsys #tors #acmtors