57 citations · 106 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…