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Philip S. Yu

4 papers here

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

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.IR2
  • cs.LG2
same name
  • Philip S. Yu — 223 papers, h 168
  • Philip S. Yu — 64 papers
  • Philip S. Yu — 36 papers, h 45
  • Philip S. Yu — 28 papers, h 10
  • Philip S. Yu — 28 papers, h 11
  • Philip S. Yu — 22 papers, h 7

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 citedPeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

4 citations · 4 across the 2 of their papers we have counts for

collaborators

4 papers

cs.IR2026

Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback

Weizhi Zhang, Wooseong Yang, Yuxin Cui +9

Traditional recommender systems (RecSys) primarily infer user preferences from implicit signals (such as clicks, watches, and purchases), often neglecting the rich explicit context…

cs.LG2024★ 4 cited

PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

Ronghui Xu, Hao Miao, Senzhang Wang +2

With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. I…

cs.LG2024

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

Wei Ju, Siyu Yi, Yifan Wang +10

Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…

cs.IR2024

Large Language Model Simulator for Cold-Start Recommendation

Feiran Huang, Yuanchen Bei, Zhenghang Yang +6

Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely sol…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.