57 citations · 132 across the 16 of their papers we have counts for
16 papers
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…
Sequential Recommendation with Diffusion Models
Hanwen Du, Huanhuan Yuan, Zhen Huang +2
Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods requi…
Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
Yufeng Zhang, Weiqing Wang, Hongzhi Yin +3
Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is t…
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…
Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
Jiaan Wang, Beiqi Zou, Zhixu Li +4
Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context. The key challenges of the task lie in…
Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +4
Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Int…
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…
Graph Factorization Machines for Cross-Domain Recommendation
Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3
Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Jian Liu, Pengpeng Zhao, Yanchi Liu +5
When purchasing appearance-first products, e.g., clothes, product appearance aesthetics plays an important role in the decision process. Moreover, user's aesthetic preference, whic…
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation
Pengpeng Zhao, Haifeng Zhu, Yanchi Liu +3
Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be…