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20212025
most citedTowards Long-term Fairness in Recommendation

197 citations · 387 across the 12 of their papers we have counts for

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Showing cs.IRShow all

9 papers · 1 filter

cs.IR2025★ 4 cited

Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems

Langming Liu, Wanyu Wang, Chi Zhang +6

Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, curre…

cs.IR2024★ 13 cited

Efficient and Robust Regularized Federated Recommendation

Langming Liu, Wanyu Wang, Xiangyu Zhao +9

Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predo…

cs.IR2024★ 86 cited

LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems

Langming Liu, Xiangyu Zhao, Chi Zhang +7

Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanism…

cs.IR2024★ 1 cited

NoteLLM-2: Multimodal Large Representation Models for Recommendation

Chao Zhang, Haoxin Zhang, Shiwei Wu +6

Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks. However, their potential in multimodal representation, particularl…

cs.IR2023★ 7 cited

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

Ziru Liu, Kecheng Chen, Fengyi Song +4

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, th…

cs.IR2023★ 30 cited

User Retention-oriented Recommendation with Decision Transformer

Kesen Zhao, Lixin Zou, Xiangyu Zhao +2

Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL…