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Wayne Xin Zhao

12 papers hereh-index 6105 citations15 works total

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

author position
  • middle author12

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.IR4
  • cs.SE2
  • cs.AI1
  • cs.LG1
same name
  • Wayne Xin Zhao — 108 papers, h 72
  • Wayne Xin Zhao — 17 papers, h 8
  • Wayne Xin Zhao — 9 papers, h 4
  • Wayne Xin Zhao — 8 papers, h 4
  • Wayne Xin Zhao — 6 papers, h 3
  • Wayne Xin Zhao — 4 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 citedRevisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

2 citations · 3 across the 11 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

ClawRec: A Claw-Native Recommender System

Chenghao Wu, Kesha Ou, Xiaolei Wang +8

Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…

cs.IR2025

Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking

Ruiyang Ren, Yuhao Wang, Junyi Li +4

In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate information seeking. Users frequent…

cs.IR2024★ 2 cited

Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

Chen Yang, Sunhao Dai, Yupeng Hou +4

Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However…

cs.IR2024

The 2nd Workshop on Recommendation with Generative Models

Wenjie Wang, Yang Zhang, Xinyu Lin +7

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This works…

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