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Victor S. Sheng

9 papers hereh-index 203.1k citations87 works total

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

author position
  • middle author5
  • last author3

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.IR8
  • cs.AI1
same name
  • Victor S. Sheng — 8 papers, h 4
  • Victor S. Sheng — 4 papers, h 20
  • Victor S. Sheng — 3 papers, h 8
  • Victor S. Sheng — 3 papers, h 2
  • Victor S. Sheng — 3 papers, h 2
  • Victor S. Sheng — 2 papers, h 6

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 citedWhere to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

46 citations · 51 across the 9 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.IR2023

Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation

Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao +3

The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention c…

cs.IR2023

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

Yongjing Hao, Pengpeng Zhao, Junhua Fang +5

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…

cs.AI2023★ 1 cited

Sequential Recommendation with Probabilistic Logical Reasoning

Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian +3

Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR…

cs.IR2023

Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation

Xinyu Du, Huanhuan Yuan, Pengpeng Zhao +5

Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preferenc…

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