TY  - STD
AU  - Hao Wu
AU  - Alejandro Ariza-Casabona
AU  - Bartłomiej Twardowski
AU  - Tri Kurniawan Wijaya
PY  - 2023//
TI  - MM-GEF: Multi-modal representation meet collaborative filtering
N2  - In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods.
UR  - https://arxiv.org/abs/2308.07222
L1  - http://158.109.8.37/files/WAT2023.pdf
N1  - LAMP
ID  - Hao Wu2023
ER  -