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researcher

Hui Fang

4 papers hereh-index 171.4k citations45 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.IR3
  • cs.LG1
same name
  • Hui Fang — 11 papers, h 3
  • Hui Fang — 5 papers, h 10
  • Hui Fang — 5 papers, h 9
  • Hui Fang — 4 papers, h 2
  • Hui Fang — 3 papers, h 3
  • Hui Fang — 2 papers, h 8

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
20192024
most citedResearch Commentary on Recommendations with Side Information: A Survey and Research Directions

192 citations · 217 across the 4 of their papers we have counts for

collaborators

4 papers

cs.IR2024

Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation

Yunwen Xia, Hui Fang, Jie Zhang +1

Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to traditio…

cs.LG2022

Decentralized Matrix Factorization with Heterogeneous Differential Privacy

Wentao Hu, Hui Fang

Conventional matrix factorization relies on centralized collection of users' data for recommendation, which might introduce an increased risk of privacy leakage especially when the…

cs.IR2022★ 25 cited

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Zhu Sun, Hui Fang, Jie Yang +5

Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproduci…

cs.IR2019★ 192 cited

Research Commentary on Recommendations with Side Information: A Survey and Research Directions

Zhu Sun, Qing Guo, Jie Yang +4

Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…

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