46 citations · 51 across the 9 of their papers we have counts for
9 papers
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao +3
The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention c…
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Junhua Fang +5
Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…
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…
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…
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…
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…