Abstract
Social recommendation leverages the social connections between users to mitigate the issue of data sparsity and enhance recommendation quality. Although existing related works show their effectiveness, there remain two critical questions: i) The patterns of preference interactions among users are varied and heterogeneous. Current models struggle to accurately capture preference shifts from user interactions in noisy social environments. ii) Existing methods handle the integration of auxiliary information coarsely, potentially introducing noise and leading to biases in user preferences. To address the limitations above, we introduce a novel framework named Robust Graph Based Social Recommendation Through Contrastive Multi-View Learning (RGCML). This framework leverages denoised social relations and global intents as dual auxiliary information sources to provide comprehensive characterization of users. Firstly, RGCML employs the concept of opinion dynamics to simulate how user preferences evolve due to noisy social relations. Then, it utilizes a specifically designed information fusion module to extract critical contextual information from multiple semantic perspectives, thereby achieving personalized information fusion. Finally, it adopts the designed global-local contrastive learning paradigm that untangles and discriminates user preferences from global intents, further addressing the noise problem and enhancing the quality of user representations. Extensive experiments conducted on three real-world datasets demonstrate the superior performance of RGCML compared to several state-of-the-art (SOTA) baselines.
Original language | English |
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Title of host publication | Special Track on AI Alignment |
Editors | Toby Walsh, Julie Shah, Zico Kolter |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 12890-12898 |
Number of pages | 9 |
Edition | 12 |
ISBN (Electronic) | 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978 |
DOIs | |
State | Published - 11 Apr 2025 |
Event | 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States Duration: 25 Feb 2025 → 4 Mar 2025 |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Number | 12 |
Volume | 39 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Conference
Conference | 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 |
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Country/Territory | United States |
City | Philadelphia |
Period | 25/02/25 → 4/03/25 |