activity
20182025
most citedDeep Conversational Recommender in Travel

33 citations · 44 across the 6 of their papers we have counts for

collaborators

8 papers

cs.IR2025

Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model

He Chang, Jie Wu, Zhulin Tao +3

Temporal Knowledge Graph Forecasting (TKGF) aims to predict future events based on the observed events in history. Recently, Large Language Models (LLMs) have exhibited remarkable…

cs.CR20243 cited

AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models

Yongheng Zhang, Tingwen Du, Yunshan Ma +5

Attack knowledge graph construction seeks to convert textual cyber threat intelligence (CTI) reports into structured representations, portraying the evolutionary traces of cyber at…

cs.IR2023

Enhancing Item-level Bundle Representation for Bundle Recommendation

Xiaoyu Du, Kun Qian, Yunshan Ma +1

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn represe…

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

Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media

Yunshan Ma, Xun Yang, Lizi Liao +2

Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves…

cs.IR2019

Automatic Fashion Knowledge Extraction from Social Media

Yunshan Ma, Lizi Liao, Tat-Seng Chua

Fashion knowledge plays a pivotal role in helping people in their dressing. In this paper, we present a novel system to automatically harvest fashion knowledge from social media. I…