Xingwu Liu
Biographic Data
| ID | 9041196 |
|---|---|
| NAME | Xingwu Liu |
| GIVEN NAMES | Xingwu |
| FAMILY NAME | Liu |
| SIGNATURE | LIU X |
| AFFILIATIONS | Institute of Computing Technology |
| ORCID | 0000-0001-8304-3126 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2003 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
MLKT4Rec
Personalized exercise recommendation is an important task in educational data mining, aiming to recommend exercises that match students’ intentions and abilities. However, existing recommendation methods often ignore the dynamic changes and individual differences in students’ knowledge levels and face serious data sparsity problems. To address these limitations, we employ graph neural networks (GNNs) to learn node representations in exercise reco…
Evaluation of the capability of interaction
Interaction, as an important computing paradigm, is far beyond incremental technological development. Various models such as interaction machines and process algebras have been developed to help design and analyze interaction systems. However, little is reported on the computability theory of interaction, and the design and analysis still have to be performed in an ad hoc way. Hence, the question is: how to uniformly evaluate the power of interac…
No prominent works on this page.
Evaluation of the capability of interaction
Interaction, as an important computing paradigm, is far beyond incremental technological development. Various models such as interaction machines and process algebras have been developed to help design and analyze interaction systems. However, little is reported on the computability theory of interaction, and the design and analysis still have to be performed in an ad hoc way. Hence, the question is: how to uniformly evaluate the power of interac…
MLKT4Rec
Personalized exercise recommendation is an important task in educational data mining, aiming to recommend exercises that match students’ intentions and abilities. However, existing recommendation methods often ignore the dynamic changes and individual differences in students’ knowledge levels and face serious data sparsity problems. To address these limitations, we employ graph neural networks (GNNs) to learn node representations in exercise reco…
Computer Science (2 works) · Artificial Intelligence (1 work) · Computability, Logic, AI Algorithms (1 work) · Distributed systems and fault tolerance (1 work) · Hierarchy (1 work) · Human–computer interaction (1 work) · Intelligent Tutoring Systems and Adaptive Learning (1 work) · Interaction Design (1 work) · Interaction model (1 work) · Logic, programming, and type systems (1 work)