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researcher

Huan Zhao

14 papers hereh-index 253.3k citations70 works total

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

author position
  • first author5
  • middle author9

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.IR5
  • cs.SI4
  • cs.CL3
  • cs.LG2
same name
  • Huan Zhao — 18 papers, h 12
  • Huan Zhao — 11 papers, h 15
  • Huan Zhao — 9 papers, h 27
  • Huan Zhao — 8 papers, h 5
  • Huan Zhao — 6 papers, h 7
  • Huan Zhao — 6 papers, h 17

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
20172023
most citedExploring the Feasibility of ChatGPT for Event Extraction

56 citations · 194 across the 14 of their papers we have counts for

collaborators
Showing cs.SIShow all

4 papers · 1 filter

cs.SI2020

Vertex-reinforced Random Walk for Network Embedding

Wenyi Xiao, Huan Zhao, Vincent W. Zheng +1

In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW),…

cs.SI2019

Ranking Users in Social Networks with Motif-based PageRank

Huan Zhao, Xiaogang Xu, Yangqiu Song +3

PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which r…

cs.SI2019★ 2 cited

Motif Enhanced Recommendation over Heterogeneous Information Network

Huan Zhao, Yingqi Zhou, Yangqiu Song +1

Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and…

cs.SI2017★ 1 cited

Collaborative Filtering with Social Local Models

Huan Zhao, Quanming Yao, James T. Kwok +1

Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restric…

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