Tag: RecommenderSystems
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RecSys Summer School 2024
📢Highlights from the 2024 RecSys Summer School in Bari, Italy! 🌍From October 8 to 12, the 2024 RecSys Summer School took place as a pre-program of this year’s ACM RecSys Conference. Co-funded by SIGCHI via its conference development fund, the school gathered leading experts to explore the latest developments in recommender systems. One of the…
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Publications @ RecSys 2024
🌟 Proud moment for our lab! 🌟 Ahmadou Wagne, a PhD student at our lab, recently presented a poster at Hashtag#RecSys2024 on “What to compare? Towards understanding user sessions on price comparison platforms,” co-authored with Julia Neidhardt (TU Wien). The research sheds light on the diverse behaviors of users on price comparison platforms, identifying six distinct session…
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Doctoral Symposium @ ACM RecSys 2024
We are pleased to share that Thomas Kolb was accepted to the Doctoral Symposium at ACM RecSys 2024. Thomas presented his ongoing research on cross-domain recommender systems, particularly focusing on large language models (LLMs) and fairness aspects in recommendations. Congratulations, Thomas, on this important achievement and for contributing valuable insights to the field! Hashtag#RecSys2024 Hashtag#DoctoralSymposium…
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RecSys Workshop 2024: Exploring AI Collaboration🤝
The RecSys Workshop held at TU Wien on September 9th and 10th, 2024, brought together leading minds in the field of recommender systems to explore the evolving collaboration between humans and AI. Organized by the Christian Doppler Lab for Recommender Systems, this event aimed to delve into the complexities and opportunities of AI-human collaboration in…
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Unlocking the Potential of Content-Based Restaurant Recommender Systems: Insights from ENTER Conference 2024
Today, Thomas E. Kolb, had the pleasure of presenting our work, “Unlocking the Potential of Content-Based Restaurant Recommender Systems” at the ENTER Konferenze 2024, in Izmir, Turkey. 📚 Our research focuses on optimizing dining experiences by examining the effectiveness of TF-IDF and SBERT models. This study not only assesses these models on a technical level…