most citedExploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

147 citations · 221 across the 6 of their papers we have counts for

collaborators

7 papers

cs.IR2022

Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation

Ruihong Qiu, Zi Huang, Hongzhi Yin

Overfitting has long been considered a common issue to large neural network models in sequential recommendation. In our study, an interesting phenomenon is observed that overfittin…

cs.IR20212 cited

Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

Ruihong Qiu, Zi Huang, Hongzhi Yin

The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequ…

cs.IR202135 cited

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

Ruihong Qiu, Sen Wang, Zhi Chen +2

Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference. It is commonly observed that user's attention to…

cs.IR2021147 cited

Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

Ruihong Qiu, Zi Huang, Jingjing Li +1

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and m…

cs.IR202137 cited

Exploiting Positional Information for Session-based Recommendation

Ruihong Qiu, Zi Huang, Tong Chen +1

For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect…

cs.CV2021

Mitigating Generation Shifts for Generalized Zero-Shot Learning

Zhi Chen, Yadan Luo, Sen Wang +3

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…