Research output per year
Research output per year
Project Details
Description
Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trust network searching/matching, and trustworthy/malicious user prediction in online social networks. This project is significant as it will advance the knowledge base for enabling a trustworthy social networking environment, benefiting billions of Australian and worldwide online social network users.
| Acronym | DP230100676 |
|---|---|
| Status | Active |
| Effective start/end date | 9/01/23 → 8/01/26 |
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Few-shot causal representation learning for out-of-distribution generalization on heterogeneous graphs
Ding, P., Wang, Y., Liu, G., Wang, N. & Zhou, X., Apr 2025, In: IEEE Transactions on Knowledge and Data Engineering. 37, 4, p. 1804-1818 15 p.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
A hierarchical and disentangling interest learning framework for unbiased and true news recommendation
Wang, S., Wang, W., Zhang, X., Wang, Y., Liu, H. & Chen, F., 2024, KDD'24: proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery, p. 3200-3211 12 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
Open AccessFile3 Citations (Scopus)1 Downloads (Pure) -
Large language models for intent-driven session recommendations
Sun, Z., Liu, H., Qu, X., Feng, K., Wang, Y. & Ong, Y-S., 2024, SIGIR'24: proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY: Association for Computing Machinery (ACM), p. 324-334 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
10 Citations (Scopus)