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researcher

Peng Jiang

30 papers hereh-index 265.5k citations40 works total

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

author position
  • middle author18
  • last author9

Across the 27 of 30 papers where every author was matched, so the position is known.

fields
  • cs.IR22
  • cs.LG7
  • cs.CL1
same name
  • Peng Jiang — 21 papers, h 4
  • Peng Jiang — 18 papers
  • Peng Jiang — 18 papers, h 4
  • Peng Jiang — 17 papers, h 4
  • Peng Jiang — 16 papers, h 9
  • Peng Jiang — 15 papers, h 4

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
20182023
most citedBERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

262 citations · 916 across the 30 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019

Compositional Network Embedding

Tianshu Lyu, Fei Sun, Peng Jiang +2

Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existin…

cs.IR2019★ 11 cited

Personalized Re-ranking for Recommendation

Changhua Pei, Yi Zhang, Yongfeng Zhang +6

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optim…

cs.IR2019★ 262 cited

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

Fei Sun, Jun Liu, Jian Wu +4

Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural ne…

cs.IR2019★ 13 cited

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

Changhua Pei, Xinru Yang, Qing Cui +5

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-…

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