28 citations · 150 across the 50 of their papers we have counts for
14 papers · 1 filter
Intent Propagation Contrastive Collaborative Filtering
Haojie Li, Junwei Du, Guanfeng Liu +3
Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommend…
ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
Xiaofan Zhou, Kyumin Lee
Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historic…
Multi-Modality is All You Need for Transferable Recommender Systems
Youhua Li, Hanwen Du, Yongxin Ni +4
ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys.…
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…
Invariant representation learning for sequential recommendation
Xiaofan Zhou
Sequential recommendation involves automatically recommending the next item to users based on their historical item sequence. While most prior research employs RNN or transformer m…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…
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…
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…
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…
Overcoming Data Sparsity in Group Recommendation
Hongzhi Yin, Qinyong Wang, Kai Zheng +2
It has been an important task for recommender systems to suggest satisfying activities to a group of users in people's daily social life. The major challenge in this task is how to…
TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung +3
Contagions (e.g. virus, gossip) spread over the nodes in propagation graphs. We can use the temporal and textual data of the nodes to compute the edge weights and then generate sub…
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…
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation
Weiqing Wang, Hongzhi Yin, Ling Chen +3
With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting v…