Details

Autor(en) / Beteiligte

• Hou, Yuquan
• Li, Caimao
• Li, Hao
• Lin, Hao
• Chen, Qiuhong
• Wang, Hongzhi
• Song, Xianhua
• Lu, Zeguang
• Wang, Yang
• Zhu, Guobin
• Han, Qilong

Titel

Focusing on the Importance of Features for CTR Prediction

Ist Teil von

• Data Science, p.41-52

Ort / Verlag

Singapore: Springer Nature Singapore

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Traditional CTR recommendation models have concentrated on how to learn low-order and high-order characteristics. The majority of them make many efforts at combining low-order and high-order functions. However, they ignore the importance of the attention mechanism for learning input features. The ECABiNet model is proposed in this article to enhance the performance of CTR. On the one hand, the ECABiNet model can learn the importance of features dynamically via the LayerNorm and ECANET layers. On the other hand, through the use of a bi-interaction layer and a DNN layer, it is capable of effectively learning the feature interactions. According to the experimental results on two public datasets, the ECABiNet model is more effective than the previous CTR model.