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Dong Wang

9 papers hereh-index 131.1k citations41 works total

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

author position
  • middle author6
  • last author3

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.IR4
  • cs.LG3
  • cs.SI2
same name
  • Dong Wang — 40 papers, h 29
  • Dong Wang — 19 papers, h 32
  • Dong Wang — 16 papers
  • Dong Wang — 15 papers, h 29
  • Dong Wang — 14 papers, h 10
  • Dong Wang — 13 papers, h 22

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
20202024
most citedExtracting Attentive Social Temporal Excitation for Sequential Recommendation

10 citations · 20 across the 8 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2024★ 1 cited

ContextGNN: Beyond Two-Tower Recommendation Systems

Yiwen Yuan, Zecheng Zhang, Xinwei He +10

Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key l…

cs.IR2021

TPRM: A Topic-based Personalized Ranking Model for Web Search

Minghui Huang, Wei Peng, Dong Wang

Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between que…

cs.IR2021★ 2 cited

GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback

Minghui Huang, Dong Wang, Shuang Liu +1

Query expansion with pseudo-relevance feedback (PRF) is a powerful approach to enhance the effectiveness in information retrieval. Recently, with the rapid advance of deep learning…

cs.IR2021★ 4 cited

Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation

Zhe Xie, Chengxuan Liu, Yichi Zhang +3

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism…

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