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Wenqi Fan

The Hong Kong Polytechnic University

32 papers hereh-index 295.9k citations60 works total

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

author position
  • first author10
  • middle author20

Across the 30 of 32 papers where every author was matched, so the position is known.

fields
  • cs.IR17
  • cs.LG7
  • cs.CL4
  • cs.SI2
  • cs.AI1
  • cs.MM1
affiliations
  • The Hong Kong Polytechnic University
same name
  • Wenqi Fan — 35 papers, h 13
  • Wenqi Fan — 15 papers, h 3
  • Wenqi Fan — 11 papers, h 7
  • Wenqi Fan — 10 papers, h 8
  • Wenqi Fan — 4 papers, h 1
  • Wenqi Fan — 4 papers, h 1

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
20192024
most citedRecommender Systems in the Era of Large Language Models (LLMs)

422 citations · 815 across the 32 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.AI2021★ 2 cited

Trustworthy AI: A Computational Perspective

Haochen Liu, Yiqi Wang, Wenqi Fan +6

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human soci…

cs.IR2021★ 1 cited

Graph Trend Filtering Networks for Recommendations

Wenqi Fan, Xiaorui Liu, Wei Jin +3

Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict ho…

cs.LG2021★ 6 cited

Jointly Attacking Graph Neural Network and its Explanations

Wenqi Fan, Wei Jin, Xiaorui Liu +7

Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…

cs.IR2021★ 5 cited

AutoLoss: Automated Loss Function Search in Recommendations

Xiangyu Zhao, Haochen Liu, Wenqi Fan +3

Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…

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