The 13th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’26), held in conjunction with RecSys, adopts a human-centered perspective on recommender systems in an era shaped by Large Language Models (LLMs), agentic AI, and generative interfaces. The workshop positions recommender syst...
Peter Brusilovsky, M. de Gemmis, Alexander Felfernig et al.· Proceedings of the 20th ACM...· 0 citations
Agentic Conversational Recommender Systems (ACRSs) are designed to recommend through multi-turn dialogue with users whose needs are not fully formed at the outset. However, their evaluation almost exclusively relies on user simulators that instantiate users with clear, pre-formed needs, reducing the interaction to a re...
Alessandro Petruzzelli, Alessandro Francesco Maria Martina, C. Musto et al.· Proceedings of the 20th ACM...· 1 citation
This work introduces a family of open-weight user simulation models capable of generalizing across diverse e-commerce domains and operationalizes three distinct behavioral stereotypes, highlighting the necessity of a scalable framework for rigorously stress-testing the next generation of conversational agents against r...
Alessandro Petruzzelli, Alessandro Francesco Maria Martina, C. Musto et al.· Information Systems Frontier...· 0 citations
The proposed REKALM, a comprehensive integration framework for enhancing LLM-based recommenders through knowledge integration, demonstrates that augmenting LLMs with lexicalized, domain-specific knowledge is an effective system-level strategy for advancing the next generation of recommender systems.
Alessandro Petruzzelli, C. Musto, Marco De Gemmis et al.· ACM Transactions on Informat...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
