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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-07082021-135758


Tipo di tesi
Tesi di laurea magistrale
Autore
SEPE, PIERPAOLO
URN
etd-07082021-135758
Titolo
Learning graph-based multimodal embeddings for fashion items recommendation
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Prof. Bacciu, Davide
relatore Trincavelli, Marco
relatore Deligiorgis, Georgios
Parole chiave
  • bert
  • bert4rec
  • deep learning
  • embeddings
  • gat
  • gcn
  • graph neural networks
  • machine learning
  • recommendation systems
  • sage
  • sequential recommendations
  • transformers
Data inizio appello
23/07/2021
Consultabilità
Tesi non consultabile
Riassunto
roduct recommendation is of paramount importance for improving customer experience in online retail. In this work, we propose to tackle the problem by integrating BERT4Rec with a deep graph network that allows learning an item representation fusing multimodal information, namely visual, textual, and transaction history. We validate our approach on an industrial scale dataset and we demonstrate increased recommendation performance with respect to BERT4Rec using only a single source of information for the items. The thesis has been developed in collaboration with H&M.
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