no code implementations • 7 Sep 2023 • Mateo Gutierrez Granada, Dina Zilbershtein, Daan Odijk, Francesco Barile
This paper investigates how large language models (LLMs) can enhance recommender systems, with a specific focus on Conversational Recommender Systems that leverage user preferences and personalised candidate selections from existing ranking models.
no code implementations • 17 Sep 2022 • Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, Maarten de Rijke
In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models.