Weike Pan

A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

2023

Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly existed in …

BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation

2024 · arXiv (Cornell University)

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of …

Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation

2024

Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there …

FedHoG: Federated Homogeneous Graph Neural Network for Privacy-Preserving Recommendation

2026 · ACM Transactions on Information Systems

Most existing GNN-based recommendation methods focus on exploiting a user–item heterogeneous graph, which, however, will cause efficiency and effectiveness challenges, in a federated learning setting considering user privacy. We find that a user–user or item–item …

Heterogeneous Graph Transfer Learning for Diversity-enhanced Cross-Domain Sequential Recommendation

2026 · ACM Transactions on Recommender Systems

Cross-domain Sequential Recommendation (CDSR) aims to alleviate the data sparsity problem in recommender systems by leveraging auxiliary information from other domains to better capture users’ sequential preferences. However, existing CDSR methods still face several limitations. …

FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation

2020

Sequential recommendation has been a hot research topic because of its practicability and high accuracy by capturing the sequential information. As deep learning (DL) based methods being widely adopted to model the local and dynamic …