This work provides a practical framework for accelerating the development of conversational recommendation agents in industry, addressing challenges that are becoming increasingly central to recommender systems and agentic applications as natural-language interfaces reach widespread adoption.
Enrico Palumbo, Alexandre Tamborrino, Victor Ode et al.· Proceedings of the 20th ACM...· 0 citations
NEO is introduced, a framework that adapts a pre-trained decoder-only LLM into a tool-free, catalog-grounded generator and treats SIDs as a distinct modality and study design choices for integrating discrete entity representations into LLMs via staged alignment and instruction tuning.
Marco De Nadai, Edoardo D'Amico, Maksym Lefarov et al.· Proceedings of the 32nd ACM...· 0 citations
Large language models (LLMs) are increasingly applied to recommendation, retrieval, and reasoning, yet deploying a single end-to-end model that can jointly support these behaviors over large, heterogeneous catalogs remains challenging. Such systems must generate unambiguous references to real items, handle multiple ent...
Marco De Nadai, Edoardo D'Amico, Maksym Lefarov et al.· Proceedings of the 32nd ACM...· 0 citations
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