57 citations · 106 across the 17 of their papers we have counts for
17 papers
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Jiaan Wang, Jianfeng Qu, Yunlong Liang +4
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence. Nevertheless, existing CKGs are…
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
Xinyu Du, Huanhuan Yuan, Pengpeng Zhao +4
The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation…
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Hanwen Du, Huanhuan Yuan, Pengpeng Zhao +4
Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network…
Sequential Recommendation with Probabilistic Logical Reasoning
Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian +3
Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR…
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Xinyu Du, Huanhuan Yuan, Pengpeng Zhao +5
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preferenc…
Meta-optimized Contrastive Learning for Sequential Recommendation
Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao +4
Contrastive Learning (CL) performances as a rising approach to address the challenge of sparse and noisy recommendation data. Although having achieved promising results, most exist…
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Hanwen Du, Hui Shi, Pengpeng Zhao +5
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation. It maximizes the agreements between paired sequence augmentation…
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +1
Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of lin…
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Xuefeng Xian +5
Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. H…
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
Yunyi Li, Pengpeng Zhao, Guanfeng Liu +4
Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation s…
Quaternion-Based Graph Convolution Network for Recommendation
Yaxing Fang, Pengpeng Zhao, Guanfeng Liu +4
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to…
A Unified Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Jun Zhou +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help…
Social Group Query Based on Multi-fuzzy-constrained Strong Simulation
Guliu Liu, Lei Li, Guanfeng Liu +1
Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participa…
Cross-Domain Recommendation: Challenges, Progress, and Prospects
Feng Zhu, Yan Wang, Chaochao Chen +3
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information f…
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
Chaochao Chen, Jamie Cui, Guanfeng Liu +2
Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However,…
A Deep Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Chaochao Chen +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender system…
Updates-Aware Graph Pattern based Node Matching
Guohao Sun, Guanfeng Liu, Yan Wang +1
Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in man…