Xuanjing Chen

Ziqing Yin, Xuanjing Chen, Xi Zhang

The rapid proliferation of AI-generated content (AIGC) has reshaped the dynamics of digital marketing and online consumer behavior. However, predicting the diffusion trajectory and market impact of such content remains challenging due to data heterogeneity, non linear propagation mechanisms, and evolving consumer interactions. This study proposes an AI driven Decision Support System (DSS) that integrates multi source data including social media streams, marketing expenditure records, consumer engagement logs, and sentiment dynamics using a hybrid Graph Neural Network (GNN) and Temporal Transformer framework. The model jointly learns the content diffusion structure and temporal influence evolution through a dual channel architecture, while causal inference modules disentangle the effects of marketing stimuli on return on investment (ROI) and market visibility. Experiments on large scale real-world datasets collected from multiple online platforms such as Twitter, TikTok, and YouTube advertising show that our system outperforms existing baselines in all six metrics. The proposed DSS enhances marketing decisions by providing interpretable real-time insights into AIGC driven content dissemination and market growth patterns.

Tingyu Yuan, Xi Zhang, Xuanjing Chen

In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI-driven framework for enterprise financial audits and high-risk identification, leveraging machine learning to improve efficiency and accuracy. Using a dataset from the Big Four accounting firms (EY, PwC, Deloitte, KPMG) from 2020 to 2025, the research examines trends in risk assessment, compliance violations, and fraud detection. The dataset includes key indicators such as audit project counts, high-risk cases, fraud instances, compliance breaches, employee workload, and client satisfaction, capturing both audit behaviors and AI's impact on operations. To build a robust risk prediction model, three algorithms - Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) - are evaluated. SVM uses hyperplane optimization for complex classification, RF combines decision trees to manage high-dimensional, nonlinear data with resistance to overfitting, and KNN applies distance-based learning for flexible performance. Through hierarchical K-fold cross-validation and evaluation using F1-score, accuracy, and recall, Random Forest achieves the best performance, with an F1-score of 0.9012, excelling in identifying fraud and compliance anomalies. Feature importance analysis reveals audit frequency, past violations, employee workload, and client ratings as key predictors. The study recommends adopting Random Forest as a core model, enhancing features via engineering, and implementing real-time risk monitoring. This research contributes valuable insights into using machine learning for intelligent auditing and risk management in modern enterprises.

Ruihan Luo, Xuanjing Chen, Ziyang Ding

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.

Zheqi Hu, Xuanjing Chen, Jinlin Hu

With the rapid growth of AI-generated content (AIGC) across domains such as music, video, and literature, the demand for emotionally aware recommendation systems has become increasingly important. Traditional recommender systems primarily rely on user behavioral data such as clicks, views, or ratings, while neglecting users' real-time emotional and intentional states during content interaction. To address this limitation, this study proposes a Multi-Modal Emotion and Intent Recognition Model (MMEI) based on a BERT-based Cross-Modal Transformer with Attention-Based Fusion, integrated into a cloud-native personalized AIGC recommendation framework. The proposed system jointly processes visual (facial expression), auditory (speech tone), and textual (comments or utterances) modalities through pretrained encoders ViT, Wav2Vec2, and BERT, followed by an attention-based fusion module to learn emotion-intent representations. These embeddings are then used to drive personalized content recommendations through a contextual matching layer. Experiments conducted on benchmark emotion datasets (AIGC-INT, MELD, and CMU-MOSEI) and an AIGC interaction dataset demonstrate that the proposed MMEI model achieves a 4.3% improvement in F1-score and a 12.3% reduction in cross-entropy loss compared to the best fusion-based transformer baseline. Furthermore, user-level online evaluations reveal that emotion-driven recommendations increase engagement time by 15.2% and enhance satisfaction scores by 11.8%, confirming the model's effectiveness in aligning AI-generated content with users' affective and intentional states. This work highlights the potential of cross-modal emotional intelligence for next-generation AIGC ecosystems, enabling adaptive, empathetic, and context-aware recommendation experiences.