6 citations · 24 across the 13 of their papers we have counts for
13 papers
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…
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
Youlong Ding, Xueyang Wu, Hao Wang +1
The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer mode…
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…
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Dugang Liu, Pengxiang Cheng, Hong Zhu +6
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications l…
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Youlong Ding, Xueyang Wu, Zhitao Li +5
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network…
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…
Selective Transfer Learning for Cross Domain Recommendation
Zhongqi Lu, Erheng Zhong, Lili Zhao +3
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually…