基于图元网络的多行为去噪感知推荐

Multi-behavior Denoising-Aware Recommendation Based on Graph Meta-Networks

摘要

针对多行为推荐场景中多源交互数据存在噪声干扰、辅助行为与目标行为语义不一致、多任务损失权重静态分配难以适配用户个性化偏好的问题,构建一个基于图元网络的多行为去噪感知推荐模型.首先,搭建多行为感知编码器,基于图卷积实现多类型交互数据的高阶特征提取;其次,设计选择性跨行为对比学习方法,借助用户节点相似度筛选伪负样本,从用户表征层面实现去噪优化;再次,构建基于项目相似度的难负样本采样策略,优化项目表征质量;最后,引入元学习动态调权机制,自适应调整多任务损失权重.在3个真实数据集上的实验结果表明,该模型优于其他先进的推荐方法,从而验证了其有效性.

Abstract

Aiming at the problems of noise interference in multi-source interaction data, semantic inconsistencies between auxiliary behaviors and target behaviors, and the difficulty of statically allocating multi-task loss weights to adapt to users’ personalized preferences in multi-behavior recommendation scenarios, this paper proposes a multi-behavior denoising-aware recommendation model based on graph meta-networks. First, a multi-behavior-aware encoder is developed to extract high-order features from multi-type interaction data using graph convolution. Second, a selective cross-behavior contrastive learning method is designed to filter false negative samples using user node similarity, thereby achieving denoising optimization at the user representation level. Third, a hard negative sample sampling strategy based on item similarity is proposed to improve the quality of item representations. Finally, a meta-learning-based dynamic weight adjustment mechanism is implemented to adaptively adjust the multi-task loss weights. Extensive experiments and ablation studies conducted on three real-world datasets demonstrate that the proposed model outperforms various state-of-the-art recommendation methods, fully validating the effectiveness of our approach.

关键词

多行为推荐 / 去噪 / 对比学习 / 难负样本采样 / 多任务学习

Key words

multi-behavior recommendation / denoising / contrastive learning / hard negative sampling / multi-task learning

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基金资助

国家自然科学基金(62466029)

云南省创新引导与科技型企业培育计划项目(202404BQ040148)