- Title
- Data-efficient graph learning meets ethical challenges
- Creator
- Tang, Tao
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194667
- Identifier
- vital:18407
- Identifier
-
https://doi.org/10.1145/3539597.3572988
- Identifier
- ISBN:9781450394079 (ISBN)
- Abstract
- Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users and items under recommender system applications. These graph learning- based methods are data hungry, which brought a significant data efficiency challenge. In this proposal, I introduce my PhD research from three aspects: 1) Efficient privacy-preserving recommendation for imbalanced data. 2) Efficient recommendation model training for Insufficient samples. 3) Explainability in the social recommendation. Challenges and solutions of the above research problems have been proposed in this proposal. © 2023 Owner/Author.
- Publisher
- Association for Computing Machinery, Inc
- Relation
- 16th ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February to 3 March 2023, WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining p. 1218-1219
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- © 2023 Copyright held by the owner/author(s).
- Subject
- Ethical challenges; Graph machine learning; Responsible recommendation
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