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View all- Li DQu HWang J(2023)A Survey on Knowledge Graph-Based Recommender Systems2023 China Automation Congress (CAC)10.1109/CAC59555.2023.10450693(2925-2930)Online publication date: 17-Nov-2023
Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN
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Xuxin Zhang, Di Wang, Dehong Gao, Wen Jiang, Wei Ning, Yang Zhou, Chen WangAuthors Info & Claims
Xuxin Zhang, Di Wang, Dehong Gao, Wen Jiang, Wei Ning, Yang Zhou, Chen WangAuthors Info & ClaimsAbstract
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.
Supplementary Material
The video is divided into three main parts. First of all, the speaker talked about the cold start problem in CTR prediction, include the cold start problem in CTR prediction and several methods to deal with this problem. Second, the speaker introduced the framework (GF2) proposed in this paper and how to apply the framework to deal with the cold start problem in CTR prediction. Finally, the speaker used a series of experiments to demonstrate the usability and universality of the framework, several ablation experiments are also presented to illustrate the role of the different components of the framework.
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- Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN
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Published: 17 October 2022
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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management
October 17 - 21, 2022
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View all- Li DQu HWang J(2023)A Survey on Knowledge Graph-Based Recommender Systems2023 China Automation Congress (CAC)10.1109/CAC59555.2023.10450693(2925-2930)Online publication date: 17-Nov-2023
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