338 citations · 1.4k across the 188 of their papers we have counts for
25 papers · 1 filter
Model Stealing Attack against Recommender System
Zhihao Zhu, Rui Fan, Chenwang Wu +3
Recent studies have demonstrated the vulnerability of recommender systems to data privacy attacks. However, research on the threat to model privacy in recommender systems, such as…
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Qi Liu, Xuyang Hou, Defu Lian +4
Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better…
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval
Zheng Liu, Chaofan Li, Shitao Xiao +2
Dense retrieval calls for discriminative embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs)…
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity
Zhihao Zhu, Chenwang Wu, Rui Fan +4
Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However,…
Invariant Representation via Decoupling Style and Spurious Features from Images
Ruimeng Li, Yuanhao Pu, Zhaoyi Li +2
This paper considers the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist and domain labels are mis…
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Kun Yi, Qi Zhang, Wei Fan +7
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many s…
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
Mingjia Yin, Hao Wang, Xiang Xu +7
The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the con…
RecExplainer: Aligning Large Language Models for Explaining Recommendation Models
Yuxuan Lei, Jianxun Lian, Jing Yao +3
Recommender systems are widely used in online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However,…
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Qi Liu, Xuyang Hou, Haoran Jin +6
Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency…
A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems
Xu Huang, Jianxun Lian, Hao Wang +2
Recommendation systems effectively guide users in locating their desired information within extensive content repositories. Generally, a recommendation model is optimized to enhanc…
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
Gangwei Jiang, Caigao Jiang, Siqiao Xue +4
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model…
Large-Scale OD Matrix Estimation with A Deep Learning Method
Zheli Xiong, Defu Lian, Enhong Chen +2
The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current…
Toward Robust Recommendation via Real-time Vicinal Defense
Yichang Xu, Chenwang Wu, Defu Lian
Recommender systems have been shown to be vulnerable to poisoning attacks, where malicious data is injected into the dataset to cause the recommender system to provide biased recom…
Interactive Graph Convolutional Filtering
Jin Zhang, Defu Lian, Hong Xie +2
Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However,…
Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias
Zhihao Shi, Jie Wang, Fanghua Lu +5
Node representation learning on attributed graphs -- whose nodes are associated with rich attributes (e.g., texts and protein sequences) -- plays a crucial role in many important d…
C-Pack: Packed Resources For General Chinese Embeddings
Shitao Xiao, Zheng Liu, Peitian Zhang +3
We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensi…
Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
Qi Liu, Zhilong Zhou, Gangwei Jiang +2
Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g.…
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Likang Wu, Junji Jiang, Hongke Zhao +4
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved…
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Xu Huang, Jianxun Lian, Yuxuan Lei +3
Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, t…
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
Jin Chen, Zheng Liu, Xu Huang +9
The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of…
A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence
Zheli Xiong, Defu Lian, Enhong Chen +2
OD matrix estimation is a critical problem in the transportation domain. The principle method uses the traffic sensor measured information such as traffic counts to estimate the tr…
Learning to Substitute Spans towards Improving Compositional Generalization
Zhaoyi Li, Ying Wei, Defu Lian
Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…