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researcher

Jun Zhou

4 papers hereh-index 11329 citations39 works total

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

author position
  • middle author2
  • last author2

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

fields
  • cs.LG2
  • cs.IR1
  • stat.ML1
same name
  • Jun Zhou — 33 papers, h 26
  • Jun Zhou — 28 papers, h 20
  • Jun Zhou — 23 papers, h 22
  • Jun Zhou — 22 papers, h 10
  • Jun Zhou — 21 papers, h 20
  • Jun Zhou — 17 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

activity
20222024
most citedEnhancing Recommender Systems with Large Language Model Reasoning Graphs

13 citations · 25 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2024★ 12 cited

G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems

Youshao Xiao, Shangchun Zhao, Zhenglei Zhou +5

Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in col…

cs.IR2023★ 13 cited

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Yan Wang, Zhixuan Chu, Xin Ouyang +10

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behavior…

cs.LG2023

ALT: An Automatic System for Long Tail Scenario Modeling

Ya-Lin Zhang, Jun Zhou, Yankun Ren +5

In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and compu…

stat.ML2022

Robust Direct Learning for Causal Data Fusion

Xinyu Li, Yilin Li, Qing Cui +2

In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average…

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