600 citations · 1.7k across the 29 of their papers we have counts for
13 papers · 1 filter
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Xiaohan Li, Zhiwei Liu, Stephen Guo +4
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. H…
Federated Social Recommendation with Graph Neural Network
Zhiwei Liu, Liangwei Yang, Ziwei Fan +2
Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Network…
Pre-training Graph Neural Network for Cross Domain Recommendation
Chen Wang, Yueqing Liang, Zhiwei Liu +2
A recommender system predicts users' potential interests in items, where the core is to learn user/item embeddings. Nevertheless, it suffers from the data-sparsity issue, which the…
Deep Fraud Detection on Non-attributed Graph
Chen Wang, Yingtong Dou, Min Chen +3
Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. Th…
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
Jianguo Zhang, Trung Bui, Seunghyun Yoon +6
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-…
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
Yu Wang, Zhiwei Liu, Ziwei Fan +2
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold…
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Ziwei Fan, Zhiwei Liu, Jiawei Zhang +3
In order to model the evolution of user preference, we should learn user/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendat…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
Ziwei Fan, Zhiwei Liu, Lei Zheng +2
The sequential patterns within the user interactions are pivotal for representing the user's preference and capturing latent relationships among items. The recent advancements of s…
Quality-Aware Network for Face Parsing
Lu Yang, Qing Song, Xueshi Xin +2
This is a very short technical report, which introduces the solution of the Team BUPT-CASIA for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Chall…
ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation
Liangwei Yang, Zhiwei Liu, Yingtong Dou +2
Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks…
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
Zhiwei Liu, Ziwei Fan, Yu Wang +1
Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically. The essential target of it is to capture the item transition correlations.…
Quality-Aware Network for Human Parsing
Lu Yang, Qing Song, Zhihui Wang +3
How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. In order to solve this problem,…