Guandong Xu
Biographic Data
| ID | 3925552 |
|---|---|
| NAME | Guandong Xu |
| GIVEN NAMES | Guandong |
| FAMILY NAME | Xu |
| SIGNATURE | XU G |
| AFFILIATIONS | University of Technology Sydney |
| ORCID | 0000-0003-4493-6663 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 7 |
| AUTHOR COUNT | 10 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Who lets AI take over? Cross-national variation in willingness to delegate socially important roles to artificial intelligence
Delegating socially significant roles to artificial intelligence (AI) is an emerging reality, yet little is known about how publics evaluate this transfer of responsibility across contexts and countries. This study applied a structural model to a large cross-national dataset (30,994 individuals in 35 countries) to test how cognitive appraisals, affective dispositions, and contextual factors jointly shape willingness to delegate socially important…
Listwise Preference Alignment Optimization for Tail Item Recommendation
Preference alignment has achieved greater success on large language models (LLMs) and drawn broad interest in recommendation research. Existing preference alignment methods for recommendation either require explicit reward modeling or only support pairwise preference comparison. The former directly increases substantial computational costs, while the latter hinders training efficiency on negative samples. Moreover, no existing effort has explored…
Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve
Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pair-wise link. Besides, different users usually perform differently for their long-term and short-ter…
Graph-Aware Deep Fusion Networks for Online Spam Review Detection
Product reviews on e-commerce platforms play a critical role in shaping users’ purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect “spam reviews” either focus on sophisticated feature engineering with traditional classification models or rely on tuning neural networks with aggregated features. In this article, we develop a nove…
Social responses to the Covid-19 pandemic
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Detecting Community Depression Dynamics Due to Covid-19 Pandemic in Australia
The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with s…
Discovering dynamic adverse behavior of policyholders in the life insurance industry
Deep learning for misinformation detection on online social networks: A Survey and New Perspectives
Models for Community Dynamics
Advances in Knowledge Discovery and Data Mining: 17Th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, appl…
Advances in Knowledge Discovery and Data Mining: 17Th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, appl…
Models for Community Dynamics
Deep learning for misinformation detection on online social networks: A Survey and New Perspectives
Detecting Community Depression Dynamics Due to Covid-19 Pandemic in Australia
The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with s…
Discovering dynamic adverse behavior of policyholders in the life insurance industry
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Graph-Aware Deep Fusion Networks for Online Spam Review Detection
Product reviews on e-commerce platforms play a critical role in shaping users’ purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect “spam reviews” either focus on sophisticated feature engineering with traditional classification models or rely on tuning neural networks with aggregated features. In this article, we develop a nove…
Social responses to the Covid-19 pandemic
Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve
Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pair-wise link. Besides, different users usually perform differently for their long-term and short-ter…
Listwise Preference Alignment Optimization for Tail Item Recommendation
Preference alignment has achieved greater success on large language models (LLMs) and drawn broad interest in recommendation research. Existing preference alignment methods for recommendation either require explicit reward modeling or only support pairwise preference comparison. The former directly increases substantial computational costs, while the latter hinders training efficiency on negative samples. Moreover, no existing effort has explored…
Who lets AI take over? Cross-national variation in willingness to delegate socially important roles to artificial intelligence
Delegating socially significant roles to artificial intelligence (AI) is an emerging reality, yet little is known about how publics evaluate this transfer of responsibility across contexts and countries. This study applied a structural model to a large cross-national dataset (30,994 individuals in 35 countries) to test how cognitive appraisals, affective dispositions, and contextual factors jointly shape willingness to delegate socially important…
Computer Science (8 works) · Computer security (4 works) · Spam and Phishing Detection (3 works) · Complex Network Analysis Techniques (2 works) · Data mining (2 works) · Data science (2 works) · Graph theory (2 works) · Mathematics (2 works) · Medicine (2 works) · Misinformation and Its Impacts (2 works)