activity
20192023
most citedBootstrap Latent Representations for Multi-modal Recommendation

290 citations · 299 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2023

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

Tong Zhang, Yong Liu, Boyang Li +5

With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations. In contrast, Multi-Ses…

cs.IR2022★ 2 cited

Layer-refined Graph Convolutional Networks for Recommendation

Xin Zhou, Donghui Lin, Yong Liu +1

Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological…

cs.IR2022★ 290 cited

Bootstrap Latent Representations for Multi-modal Recommendation

Xin Zhou, Hongyu Zhou, Yong Liu +5

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendat…

cs.IR2022★ 2 cited

Minimalist and High-performance Conversational Recommendation with Uncertainty Estimation for User Preference

Yinan Zhang, Boyang Li, Yong Liu +2

Conversational recommendation system (CRS) is emerging as a user-friendly way to capture users' dynamic preferences over candidate items and attributes. Multi-shot CRS is designed…

cs.IR2022★ 5 cited

Enhancing Sequential Recommendation with Graph Contrastive Learning

Yixin Zhang, Yong Liu, Yonghui Xu +5

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exp…

cs.CL2019

Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition

Shengfei Lyu, Linghao Sun, Huixiong Yi +3

In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language,…