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20242026
most citedSparser Training for On-Device Recommendation Systems

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.IR2026

When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study

Shutong Qiao, Wei Yuan, Tong Chen +3

Semantic ID learning is a key interface in Generative Recommendation (GR) models, mapping items to discrete identifiers grounded in side information, most commonly via a pretrained…

cs.SI2025

Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics

Wei Jiang, Tong Chen, Wei Yuan +3

Social media platforms generate vast, complex graph-structured data, facilitating diverse tasks such as rumor detection, bot identification, and influence modeling. Real-world appl…

cs.IR2025

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model

Zhaofeng Zhong, Wei Yuan, Liang Qu +4

With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face t…

cs.IR2025

Multi-agents based User Values Mining for Recommendation

Lijian Chen, Wei Yuan, Tong Chen +3

Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that f…

cs.LG2024

Tackling Data Heterogeneity in Federated Time Series Forecasting

Wei Yuan, Guanhua Ye, Xiangyu Zhao +3

Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.…

cs.IR20241 cited

Sparser Training for On-Device Recommendation Systems

Yunke Qu, Liang Qu, Tong Chen +3

Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. Th…