# SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing

arXiv: https://arxiv.org/abs/2509.17361 · HTML: https://mlsanity.com/paper/2509.17361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning](https://mlsanity.com/paper/2403.19345/) (2024)

## Built on by (most cited first, 1 of 1)

- [CATCH: A Modular Cross-domain Adaptive Template with Hook](https://mlsanity.com/paper/2510.26582/) (2025)

## Abstract (arXiv)

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.