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20212024
most citedBipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space

21 citations · 29 across the 6 of their papers we have counts for

collaborators

6 papers

physics.soc-ph2024

On the role of network structure in learning to coordinate with bounded rationality

Yifei Zhang, Marcos M. Vasconcelos

Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common actio…

cs.MM2023

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt

Xiaocui Yang, Shi Feng, Daling Wang +5

We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Mul…

cs.IR20234 cited

WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering

Yankai Chen, Yifei Zhang, Menglin Yang +3

Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations,…

cs.IR202321 cited

Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space

Yankai Chen, Yixiang Fang, Yifei Zhang +1

Searching on bipartite graphs is basal and versatile to many real-world Web applications, e.g., online recommendation, database retrieval, and query-document searching. Given a que…

cs.LG20233 cited

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy

Yifei Zhang, Dun Zeng, Jinglong Luo +2

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands…

cs.IR20211 cited

Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation

Yankai Chen, Yifei Zhang, Yingxue Zhang +5

Due to the promising advantages in space compression and inference acceleration, quantized representation learning for recommender systems has become an emerging research direction…