6 citations · 24 across the 13 of their papers we have counts for
9 papers · 1 filter
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation
Weixin Li, Yuhao Wu, Yang Liu +2
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 hete…
Privacy-Preserving Cross-Domain Sequential Recommendation
Zhaohao Lin, Weike Pan, Zhong Ming
Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-dom…
A Survey on Multi-Behavior Sequential Recommendation
Xiaoqing Chen, Zhitao Li, Weike Pan +1
Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of mos…
GNN4FR: A Lossless GNN-based Federated Recommendation Framework
Guowei Wu, Weike Pan, Zhong Ming
Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and i…
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
Dugang Liu, Pengxiang Cheng, Zinan Lin +6
Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an…
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Dugang Liu, Yang Qiao, Xing Tang +4
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most o…
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
Zijie Zeng, Jing Lin, Weike Pan +2
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we re…
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Ruixuan Liu, Yanlin Wang, Yang Cao +4
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View
Weike Pan
Intelligent recommendation technology has been playing an increasingly important role in various industry applications such as e-commerce product promotion and Internet advertiseme…