UNITesi

This thesis explores whether emotional signals extracted by LLMs can be effectively integrated into a multistage recommender system based on LightGCN candidate generation and emotion-aware reranking on MovieLens-1M

This thesis explores whether emotional signals extracted by LLMs can be effectively integrated into a multistage recommender system based on LightGCN candidate generation and emotion-aware reranking on MovieLens-1M

EMOTION-BASED MULTISTAGE RECOMMENDER SYSTEM FOR THE MOVIE DOMAIN

SFILIO, MAURIZIO
2024/2025

Abstract

This thesis explores whether emotional signals extracted by LLMs can be effectively integrated into a multistage recommender system based on LightGCN candidate generation and emotion-aware reranking on MovieLens-1M
2024
EMOTION-BASED MULTISTAGE RECOMMENDER SYSTEM FOR THE MOVIE DOMAIN
This thesis explores whether emotional signals extracted by LLMs can be effectively integrated into a multistage recommender system based on LightGCN candidate generation and emotion-aware reranking on MovieLens-1M
Lauree magistrali a ciclo unico
File in questo prodotto:
Fulltext non presente su UNITESI – Verifica la disponibilità della tesi in formato cartaceo interrogando il Catalogo di Ateneo

I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14253/109270