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Zitao Liu

4 papers hereh-index 9225 citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.IR3
  • cs.AI1
same name
  • Zitao Liu — 49 papers, h 25
  • Zitao Liu — 13 papers, h 20
  • Zitao Liu — 11 papers, h 7
  • Zitao Liu — 3 papers, h 0
  • Zitao Liu — 1 paper, h 11
  • Zitao Liu — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedEfficient and Robust Regularized Federated Recommendation

13 citations · 13 across the 3 of their papers we have counts for

collaborators

4 papers

cs.AI2025

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning

Xiao Han, Zimo Zhao, Wanyu Wang +4

Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially when retraining…

cs.IR2025

STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation

Maolin Wang, Sheng Zhang, Ruocheng Guo +6

Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capt…

cs.IR2025

Behavior Modeling Space Reconstruction for E-Commerce Search

Yejing Wang, Chi Zhang, Xiangyu Zhao +8

Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, search systems model user behaviors by combining user p…

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…

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