Multi-source Feature Fusion and Information Flow Advertising Effectiveness Prediction Model Based on TabTransformer

Abstract

Click-through rate (CTR) prediction for feed advertisements relies on effective modeling of multi-source heterogeneous features. However, the inherent difficulty in capturing contextual semantic relationships among different feature types significantly limits model generalization. To address this issue, this paper proposes a multi-source feature fusion framework based on TabTransformer by encoding heterogeneous categorical information into a unified f eature t able. T he s elf-attention m echanism i s employed to model high-order semantic interactions, while multi-head attention dynamically captures contextual dependencies among features. Meanwhile, a parallel residual pathway integrates numerical information with Transformer representations to enhance feature complementarity. The fused representations are processed by a fully connected layer with Sigmoid activation, and model optimization is performed using cross-entropy loss. Considering that multi-source information fusion and contextual dependency modeling are also essential for intelligent electromagnetic information processing and adaptive communication systems, the proposed framework provides a data-driven reference for complex heterogeneous signal representation and decision optimization. Experimental results demonstrate that the proposed method achieves an AUC of 0.945 and a LogLoss of 0.352 in CTR prediction. In cold-start scenarios, the model maintains a LogLoss of only 0.402 for new users, while outperforming DeepFM and DCN in top-level recommendation accuracy (P@1 = 0.582). Furthermore, its probability calibration error of only 0.008 provides reliable support for advertising bidding and effectively alleviates the generalization degradation caused by insufficient user or advertisement embeddings.