Attention-based Fusion for Outfit Recommendation

08/28/2019
by   Katrien Laenen, et al.
4

This paper describes an attention-based fusion method for outfit recommendation which fuses the information in the product image and description to capture the most important, fine-grained product features into the item representation. We experiment with different kinds of attention mechanisms and demonstrate that the attention-based fusion improves item understanding. We outperform state-of-the-art outfit recommendation results on three benchmark datasets.

READ FULL TEXT

page 1

page 5

research
09/16/2022

Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation

Sequential recommendation aims to recommend the next item of users' inte...
research
04/24/2023

Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation

The main idea of multimodal recommendation is the rational utilization o...
research
04/23/2022

Decoupled Side Information Fusion for Sequential Recommendation

Side information fusion for sequential recommendation (SR) aims to effec...
research
06/01/2023

Coneheads: Hierarchy Aware Attention

Attention networks such as transformers have achieved state-of-the-art p...
research
06/18/2020

A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention

Deep neural networks (DNNs) have been widely employed in recommender sys...
research
06/10/2020

Why is Attention Not So Attentive?

Attention-based methods have played an important role in model interpret...
research
09/12/2022

SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

With the boom of e-commerce, Multimodal Review Helpfulness Prediction (M...

Please sign up or login with your details

Forgot password? Click here to reset