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researcher

Xiaoli Li

Institute for Infocomm Research

13 papers hereh-index 5314.5k citations200 works total

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

author position
  • middle author4
  • last author9

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL2
  • cs.IR2
  • q-bio.QM2
  • cs.AI1
  • cs.CV1
affiliations
  • Institute for Infocomm Research
Homepage
same name
  • Xiaoli Li — 37 papers, h 27
  • Xiaoli Li — 23 papers, h 25
  • Xiaoli Li — 8 papers, h 3
  • Xiaoli Li — 7 papers, h 10
  • Xiaoli Li — 5 papers, h 19
  • Xiaoli Li — 5 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
20152023
most citedGlobal Context Enhanced Graph Neural Networks for Session-based Recommendation

554 citations · 769 across the 12 of their papers we have counts for

collaborators
Showing 2018Show all

3 papers · 1 filter

cs.LG2018★ 10 cited

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Yong Liu, Min Wu, Chenghao Liu +2

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, s…

cs.IR2018★ 21 cited

HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems

Lucas Vinh Tran, Yi Tay, Shuai Zhang +2

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic spa…

cs.AI2018★ 34 cited

Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay +3

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challen…

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