=Paper=
{{Paper
|id=Vol-3823/0_musto_keynote
|storemode=property
|title=Keynote: Healthy and Sustainable Food Recommendations Exploiting Natural Language Processing and Large Language Models
|pdfUrl=https://ceur-ws.org/Vol-3823/0_musto_keynote.pdf
|volume=Vol-3823
|authors=Cataldo Musto
|dblpUrl=https://dblp.org/rec/conf/healthrecsys/Musto24
}}
==Keynote: Healthy and Sustainable Food Recommendations Exploiting Natural Language Processing and Large Language Models==
<pdf width="1500px">https://ceur-ws.org/Vol-3823/0_musto_keynote.pdf</pdf>
<pre>
                         Keynote: Healthy and Sustainable Food Recommendations
                         Exploiting Natural Language Processing and Large
                         Language Models
                         Cataldo Musto1
                         1
                             University of Bari, Italy


                                         Abstract
                                         The growing focus on healthy and sustainable eating requires innovative tools to support personalized recom-
                                         mendations. This talk will examine how Natural Language Processing (NLP) and Large Language Models (LLMs)
                                         can enhance food recommendation systems, enabling them to provide personalized suggestions that promote
                                         both individual well-being and environmental responsibility. In particular, we will first show the effectiveness
                                         of knowledge-aware recommendation models that encode information about healthy food consumption. Next,
                                         we emphasize the importance of natural language processing techniques, which can be used to nudge toward
                                         healthier food choices through automatically generated explanations. Finally, we will show recent advances
                                         aiming also to include the concept of sustainability in the design and development of conversational food recom-
                                         menders. In particular, we will discuss a pipeline based on LLMs that identifies healthier and more sustainable
                                         food alternatives that can be suitable for the user. We will conclude the presentation by sketching several future
                                         directions of this exciting research line.

                                         Keywords
                                         Health recommender systems, Large Language Models, healthy living, health and care, Recommender Systems




                         Bio
                         Cataldo Musto is an Associate Professor at the Department of Computer Science, University of Bari.
                         His research focuses on the adoption of NLP, LLMs, and semantic content representation strategies in
                         knowledge-aware recommender systems and AI algorithms. He authored around 90 scientific articles,
                         and he is one of the authors of the textbook “Semantics in Adaptive and Personalized Systems: Methods,
                         Tools and Applications”, edited by Springer. He is also involved in the organization of conferences such
                         as ACM UMAP and ACM RecSys as Student Volunteers Chair in 2019, Social Chair in 2020, Poster and
                         Demo Chair in 2022, Doctoral Symposium Chair in 2023, and Workshops Chair in 2024. In 2025, he will
                         be the Program Chair of ACM UMAP conference. Since 2016, he has given several tutorials at UMAP
                         and ESWC conferences about the exploitation of semantics-aware representation in content-based
                         personalized systems. Since 2019, he has organized a series of workshops on Explainable User Modeling
                         (ExUM), Knowledge-aware Recommender Systems (KARS) and Explainable Artificial Intelligence (XAI).




                          HealthRecSys’24: The 6th Workshop on Health Recommender Systems co-located with ACM RecSys 2024
                         *
                           Corresponding author.
                                        © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).


CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings

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