Click-through rate prediction model integrating user interest and multi-head attention mechanism

HIGHLIGHTS

SUMMARY

It instructs the neural_network to concentrate exclusively on the most significant aspects of the input information, therefore giving them more weight. The crucial combination of features, as well as the weight values of each feature, may be shown, ensuring that the model is easy to understand in the recommendation task. To simulate higher-order features, NFM built deep neural_networks on top of the output of second-order feature interactions. Similarly, feed-forward neural_networks were used to describe high-order feature interactions in PNN, FFM, DeepCrossing, Wide and amp; Deep, and DeepFM. The residual connection . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or E-mail:

If you don't have an account, you can create one here.