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researcher

Min Zhang

4 papers hereh-index 6127 citations14 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.IR2
  • cs.HC1
  • cs.MM1
same name
  • Min Zhang — 49 papers, h 11
  • Min Zhang — 32 papers, h 11
  • Min Zhang — 26 papers
  • Min Zhang — 23 papers, h 6
  • Min Zhang — 22 papers, h 7
  • Min Zhang — 22 papers, h 9

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
20232026
most citedAiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.MM2026

What Would You Click? Personalized Video Thumbnail Generation with Preference-aware Highlight Retrieval

Zhiyu He, Zecheng Zhao, Tong Chen +3

Video thumbnails are a key factor for attracting user clicks on video platforms, and are increasingly supported by automation. However, existing thumbnail generation methods typica…

cs.HC2024

Introducing EEG Analyses to Help Personal Music Preference Prediction

Zhiyu He, Jiayu Li, Weizhi Ma +3

Nowadays, personalized recommender systems play an increasingly important role in music scenarios in our daily life with the preference prediction ability. However, existing method…

cs.IR2024★ 1 cited

Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation

Hanyu Li, Weizhi Ma, Peijie Sun +6

Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the so…

cs.IR2023

Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation

Shenghao Yang, Chenyang Wang, Yankai Liu +7

Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually ut…

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