Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN

Click-through rate (CTR) prediction is one of the core tasks in industrial applications such as online advertising and recommender systems. However, the performance of existing CTR models is hampered by the cold-start users who have very few historical behavior data, given that these models often rely on enough sequential behavior data to learn the embedding vectors. In this paper, we propose a novel framework dubbed GF2 to alleviate the cold-start problem in deep learning based CTR prediction. GF2 augments the embeddings of cold-start users after the embedding layer in the deep CTR model based on the Generative Adversarial Network (GAN), and the obtained generator by GAN can be further fine-tuned locally to enhance the CTR prediction in cold-start settings. GF2 is general for deep CTR models that use embeddings to model the features of users, and it has already been deployed in real-world online display advertising system. Experimental results on two large-scale real-world datasets show that GF2 can significantly improve the prediction performance over three polular deep CTR models.