论文标题
通过嵌入学习框架的CTR预测中的冷启动问题
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
论文作者
论文摘要
我们提出了一个普遍的变分嵌入学习框架（VELF），以减轻CTR预测中严重的冷启动问题。 Velf通过通过两种方式来减轻由Data-Sparsity引起的过度拟合来解决冷启动问题：学习概率嵌入，并纳入可训练的和正则化的先验，以利用冷启动用户和广告（ADS）的丰富信息。这两种技术自然地整合到各种推理框架中，形成了端到端的培训过程。基准数据集上的大量经验测试很好地证明了我们提出的VELF的优势。此外，扩展实验证实，与传统的固定先验相比，我们的参数化和正则化的先验具有更多的概括能力。
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.
