Web: http://arxiv.org/abs/2201.10980

Jan. 27, 2022, 2:11 a.m. | Xiaoxiao Xu, Chen Yang, Qian Yu, Zhiwei Fang, Jiaxing Wang, Chaosheng Fan, Yang He, Changping Peng, Zhangang Lin, Jingping Shao

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv embedding framework learning prediction

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