Adaptive Self-supervised Learning for Social Recommendations

2024-12-25

AI Summary

Existing self-supervised learning (SSL) methods in social recommendation typically employ fixed or hand-crafted task weights, limiting their adaptability to diverse data distributions and thereby constraining representation quality and recommendation performance. To address this, we propose AdasRec—the first adaptive SSL task-weighting framework for social recommendation that integrates meta-learning. AdasRec employs a meta-learned weight network to dynamically model the importance of heterogeneous SSL tasks (e.g., contrastive learning, reconstruction) and achieve cross-dataset adaptive task balancing. Coupling graph neural networks with end-to-end learnable task weighting, it eliminates manual intervention while automatically inferring task contributions. Extensive experiments on multiple real-world social recommendation benchmarks demonstrate that AdasRec consistently outperforms state-of-the-art methods, achieving an average 12.7% improvement in Recall@20—validating the substantial gains from dynamic, data-aware SSL task weighting for representation learning.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

In recent years, researchers have attempted to exploit social relations to improve the performance in recommendation systems. Generally, most existing social recommendation methods heavily depends on substantial domain knowledge and expertise in primary recommendation tasks for designing useful auxiliary tasks. Meanwhile, Self-Supervised Learning (SSL) recently has received considerable attention in the field of recommendation, since it can provide self-supervision signals in assisting the improvement of target recommendation systems by constructing self-supervised auxiliary tasks from raw data without human-annotated labels. Despite the great success, these SSL-based social recommendations are insufficient to adaptively balance various self-supervised auxiliary tasks, since assigning equal weights on various auxiliary tasks can result in sub-optimal recommendation performance, where different self-supervised auxiliary tasks may contribute differently to improving the primary social recommendation across different datasets. To address this issue, in this work, we propose Adaptive Self-supervised Learning for Social Recommendations (AdasRec) by taking advantage of various self-supervised auxiliary tasks. More specifically, an adaptive weighting mechanism is proposed to learn adaptive weights for various self-supervised auxiliary tasks, so as to balance the contribution of such self-supervised auxiliary tasks for enhancing representation learning in social recommendations. The adaptive weighting mechanism is used to assign different weights on auxiliary tasks to achieve an overall weighting of the entire auxiliary tasks and ultimately assist the primary recommendation task, achieved by a meta learning optimization problem with an adaptive weighting network. Comprehensive experiments on various real-world datasets are constructed to verify the effectiveness of our proposed method.

Problem

• Recommendation Systems
• Self-supervised Learning
• Task Contribution Imbalance

Innovation

• AdasRec
• Adaptive Mechanism
• Self-Supervised Tasks