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Dear Reviewers: Don't reject because we made it look easy. Making it look easy is hard work. Don't reject because our solution is simple. Simple solutions often have the most impact. Don't reject because you can imagine additional experiments. There's a page limit.

Reviewers automatically assume that simple is not novel. This is sheer laziness. Yes, it may be simple and obvious in retrospect, but someone had to have that insight first. Simple is good. Simple is robust, easy to implement and reproduce, broadly applicable, etc.

#ROEGen@RecSys workshop explored various opportunities and risks associated with generative AI for RecSys. A big thanks to Yashar Deldjoo Scott Sanner and Enrico Palumbo for organizing and moderating the workshop. Great first step to bring the community together!

Thank you for joining Iadh Ounis It was really great to have you with us at #ROEGen. #recsys2024 #recsys

Please put your hands together one more time for our student volunteers! #RecSys2024

I am home from #RecSys2024. At the conference, I had the great joy of presenting two papers, a workshop talk, and a tutorial at the summer school. Bari is a beautiful town, and I had a lot of nice food there. Below are some of my highlights and thoughts: (1/🧵)

RecSys #recsys2024 in #Bari was a special experience for me as an attendee and as part of the organizing team as Proceedings Chair! Great talks and tutorials, especially the Bart Knijnenburg | BLM | LGBTQ+ | Yay Socialism tuto on user studies. Proud to present our work lnkd.in/d5RGMUiQ at #HealthRecSys

RecSys 2024 just concluded. I'm happy to share that our paper was nominated for the best short paper award at the conference. We developed Semantic IDs in our Generative Retrieval (arxiv.org/abs/2305.05065) work and perfected their use for large-scale recommender systems (like

This week, I’ll attend #CIKM2024 ACM CIKM 2024. A busy schedule ahead 😃 👉 Session chair (#graph #recommendation session) 👉 Short paper (arxiv.org/abs/2408.11767) w/ Emanuele Rossi Claudio Pomo Tommaso Di Noia Fragkiskos Malliaros 👉 Mentor at the PhD symposium See you at the conference!! 🚀🚀🚀

It was a great pleasure giving a teaser presentation about our ACM Transactions on Recommender Systems (ACM TORS) paper "Where are the values? A systematic literature review on news recommender systems" at #RecSys2024. Honored to receive a #WomenInRecSys journal paper of the year award for it. doi.org/10.1145/3654805

Over the last week, I was invited to give a talk at HCAI MMS (JKU Linz) and DaSciM (École polytechnique) groups regarding our (w/ Fragkiskos Malliaros Emanuele Rossi Claudio Pomo Tommaso Di Noia) on-going project on "Graph-based Imputation to Address Missing Modalities in Multimdal Recommendation" ⤵️

Last Thursday, I successfully defended my thesis! 🎓 This PhD journey has been incredible, thanks to my amazing colleagues at SisInfLab - Polytechnic of Bari and my supervisor Tommaso Di Noia 🫶. A special shoutout to Pasquale Minervini is hiring postdocs! 🚀 for making my visiting period at School of Informatics, The University of Edinburgh unforgettable! 🚀

In my thesis, ‘Designing Secure and Knowledge-Aware Recommender Systems Leveraging Data Properties and Graph Structures,’ I explored #RecSys 💡, enhancing #Graph-based models 🕸️ with knowledge graphs and safeguarding data using #DifferentialPrivacy 🔒

Xiangnan He Suzan Verberne 🤹‍♀️ Andrew Trotman At this point, as RecSys researchers, we are not even valuable as reviewers 🤷‍♂️ #sigir2025

Grateful to my co-authors Dario Di Palma Claudio Pomo Lucio Narducci Tommaso Di Noia for the shared work, and to the editorial team of Human-Centered AI for the opportunity to contribute to such a thoughtful volume. Always a pleasure to collaborate in advancing #HumanCenteredAI 🤝

The First International WS on #Data #Quality-Aware #Multimodal #Recommendation (#DaQuaMRec DaQuaMRec@RecSys2025) is happening at #RecSys2025 ACM RecSys! 🙆 Claudio Pomo Dietmar Jannach Yubin @Google Next25 Daniele Malitesta Alberto Carlo Maria Mancino Julian Mcauley Alessandro B. Melchiorre Shah Nawaz

At a coffe break of #RecSys2024, I and Dario Di Palma asked ourselves: ‘Do LLMs Memorize Recommendation Datasets?’ Then, we tried to answer it and the result of ‘A Preliminary Study on MovieLens-1M’ has been accepted at #SIGIR2025 In Pills? Big LLMs can memorize the testset :/

The data preprocessing phase in recommender systems can be long and tedious. With 🧩DataRec, that’s no longer the case! “DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems” has been accepted for publication at SIGIR 2025. 🚀🚀

🫂 A huge thank you to my co-authors Salvatore Bufi (Salvatore Bufi), Angela Di Fazio (Angela Di Fazio), Antonio Ferrara (Antonio Ferrara), Daniele Malitesta (Daniele Malitesta), Claudio Pomo (Claudio Pomo) and Tommaso Di Noia (Tommaso Di Noia)

🚀 DataRec doesn’t stop here! It’s an open and evolving project 🛠️, aiming to include more datasets 📊, new recommendation domains 🛍️, and the latest innovations in recommender systems 🧠. If you’re interested in contributing, don’t wait, get in touch with us! 🤝💬