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Jin Liu

4 papers here

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

author position
  • first author1
  • middle author3

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

fields
  • cs.IR2
  • cs.CL1
  • cs.CV1
ORCID 0000-0003-0359-0248
same name
  • Jin Liu — 11 papers, h 28
  • Jin Liu — 9 papers, h 20
  • Jin Liu — 7 papers
  • Jin Liu — 6 papers
  • Jin Liu — 4 papers
  • Jin Liu — 2 papers

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

most citedA Frequency-aware Software Cache for Large Recommendation System Embeddings

1 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

cs.IR2023

Mobile Supply: The Last Piece of Jigsaw of Recommender System

Zhenhao Jiang, Biao Zeng, Hao Feng +4

Recommendation system is a fundamental functionality of online platforms. With the development of computing power of mobile phones, some researchers have deployed recommendation al…

cs.CV2023

Asymmetric Patch Sampling for Contrastive Learning

Chengchao Shen, Jianzhong Chen, Shu Wang +3

Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning. However, there are still a mass of appearance simila…

cs.CL2023★ 1 cited

Joint embedding in Hierarchical distance and semantic representation learning for link prediction

Jin Liu, Jianye Chen, Chongfeng Fan +1

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with…

cs.IR2022★ 1 cited

A Frequency-aware Software Cache for Large Recommendation System Embeddings

Jiarui Fang, Geng Zhang, Jiatong Han +5

Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a…

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