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researcher

Yan Wang

14 papers hereh-index 233.2k citations48 works total

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

author position
  • middle author14

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.IR10
  • cs.LG2
  • cs.DB1
  • eess.IV1
same name
  • Yan Wang — 24 papers, h 8
  • Yan Wang — 24 papers, h 25
  • Yan Wang — 23 papers, h 9
  • Yan Wang — 22 papers, h 3
  • Yan Wang — 21 papers, h 16
  • Yan Wang — 20 papers, h 12

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
20192023
most citedSequential Recommender Systems: Challenges, Progress and Prospects

361 citations · 499 across the 14 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.IR2021★ 1 cited

A Unified Framework for Cross-Domain and Cross-System Recommendations

Feng Zhu, Yan Wang, Jun Zhou +3

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help…

cs.IR2021

Next-item Recommendations in Short Sessions

Wenzhuo Song, Shoujin Wang, Yan Wang +1

The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-ite…

cs.IR2021★ 10 cited

Graph Learning based Recommender Systems: A Review

Shoujin Wang, Liang Hu, Yan Wang +6

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model u…

cs.IR2021★ 16 cited

Cross-Domain Recommendation: Challenges, Progress, and Prospects

Feng Zhu, Yan Wang, Chaochao Chen +3

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information f…

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