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Click Prediction Based on Feature Fusion and Attention Mechanism

The application of deep learning in recommendation systems is mainly to capture high-level feature combinations, which can better discover the impact of cross-features on user clicks than traditional recommendation models. Differently, traditional factorization machine models work better on the low-level features. The combination of these two models has good memory and generalization capabilities for recommendation systems. However, due to the different effects of low-level features and high-level features on the user click behaviors, a straightforward combination of these two models is incompetent to predict user clicks. To address this issue, we propose ADAFM, a feature fusion model based on the attention mechanism to select and emphasize the important features. Experimental results show that our model outperforms the baseline models in terms of better ranking ability.