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researcher

Xin Zhou

9 papers hereh-index 121.1k citations22 works total

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

author position
  • first author4
  • middle author5

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

fields
  • cs.IR7
  • cs.LG2
same name
  • Xin Zhou — 63 papers, h 18
  • Xin Zhou — 34 papers, h 17
  • Xin Zhou — 21 papers, h 11
  • Xin Zhou — 18 papers, h 14
  • Xin Zhou — 17 papers, h 7
  • Xin Zhou — 13 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
20222024
most citedBootstrap Latent Representations for Multi-modal Recommendation

290 citations · 555 across the 8 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 17 cited

Inductive Graph Transformer for Delivery Time Estimation

Xin Zhou, Jinglong Wang, Yong Liu +3

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase exp…

cs.IR2022★ 233 cited

A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation

Xin Zhou, Zhiqi Shen

Multimodal recommender systems utilizing multimodal features (e.g., images and textual descriptions) typically show better recommendation accuracy than general recommendation model…

cs.IR2022★ 2 cited

Layer-refined Graph Convolutional Networks for Recommendation

Xin Zhou, Donghui Lin, Yong Liu +1

Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological…

cs.IR2022★ 290 cited

Bootstrap Latent Representations for Multi-modal Recommendation

Xin Zhou, Hongyu Zhou, Yong Liu +5

This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendat…

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