SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
This work addresses the problem of enhancing personalized advertising and content recommendations for digital platforms, representing an incremental improvement with novel method integration.
The paper tackles the problem of improving recommendation accuracy and robustness in personalized content marketing by addressing limitations like reliance on limited supervised signals and vulnerability to noisy interactions, achieving a 6.7% improvement in NDCG@10 and 11.3% improvement in HR@10 over state-of-the-art baselines.
Personalized content marketing has become a crucial strategy for digital platforms, aiming to deliver tailored advertisements and recommendations that match user preferences. Traditional recommendation systems often suffer from two limitations: (1) reliance on limited supervised signals derived from explicit user feedback, and (2) vulnerability to noisy or unintentional interactions. To address these challenges, we propose SeqUDA-Rec, a novel deep learning framework that integrates user behavior sequences with global unsupervised data augmentation to enhance recommendation accuracy and robustness. Our approach first constructs a Global User-Item Interaction Graph (GUIG) from all user behavior sequences, capturing both local and global item associations. Then, a graph contrastive learning module is applied to generate robust embeddings, while a sequential Transformer-based encoder models users' evolving preferences. To further enhance diversity and counteract sparse supervised labels, we employ a GAN-based augmentation strategy, generating plausible interaction patterns and supplementing training data. Extensive experiments on two real-world marketing datasets (Amazon Ads and TikTok Ad Clicks) demonstrate that SeqUDA-Rec significantly outperforms state-of-the-art baselines such as SASRec, BERT4Rec, and GCL4SR. Our model achieves a 6.7% improvement in NDCG@10 and 11.3% improvement in HR@10, proving its effectiveness in personalized advertising and intelligent content recommendation.