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researcher

Lei Meng

5 papers hereh-index 7269 citations11 works total

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

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.IR4
  • cs.LG1
same name
  • Lei Meng — 15 papers, h 8
  • Lei Meng — 11 papers, h 7
  • Lei Meng — 11 papers, h 11
  • Lei Meng — 6 papers, h 16
  • Lei Meng — 3 papers, h 2
  • Lei Meng — 2 papers, h 3

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
20242026
most citedNegative Sampling in Recommendation: A Survey and Future Directions

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment

Haokai Ma, Aoqi Hu, Yueao Xing +5

Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space. Recent att…

cs.IR2025

Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training

Haokai Ma, Yunshan Ma, Ruobing Xie +5

Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…

cs.IR2024★ 1 cited

Negative Sampling in Recommendation: A Survey and Future Directions

Haokai Ma, Ruobing Xie, Lei Meng +5

Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``in…

cs.IR2024

Plug-in Diffusion Model for Sequential Recommendation

Haokai Ma, Ruobing Xie, Lei Meng +4

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommend…

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