activity
20192022
most citedLeveraging Two Types of Global Graph for Sequential Fashion Recommendation

24 citations · 28 across the 5 of their papers we have counts for

collaborators

6 papers

cs.MM2022

Modeling Field-level Factor Interactions for Fashion Recommendation

Yujuan Ding, P. Y. Mok, Xun Yang +1

Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due…

cs.IR202124 cited

Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

Yujuan Ding, Yunshan Ma, Wai Keung Wong +1

Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. Th…

cs.LG2021

Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting

Yunshan Ma, Yujuan Ding, Xun Yang +5

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR…

cs.LG2021

Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media

Yujuan Ding, Yunshan Ma, Lizi Liao +2

Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devote…

cs.IR20204 cited

Knowledge Enhanced Neural Fashion Trend Forecasting

Yunshan Ma, Yujuan Ding, Xun Yang +3

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fas…

cs.CV2019

Bilinear Supervised Hashing Based on 2D Image Features

Yujuan Ding, Wai Kueng Wong, Zhihui Lai +1

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the exist…