计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 10-17.doi: 10.11896/jsjkx.210600009
帅剑波, 王金策, 黄飞虎, 彭舰
SHUAI Jian-bo, WANG Jin-ce, HUANG Fei-hu, PENG Jian
摘要： 点击率(Click-Through Rate,CTR)预测是推荐系统中一项重要的任务,其目标是预测用户点击一个广告或者商品的概率。特征嵌入和特征组合是影响预测性能的关键,因此很多点击率预测模型的思路也是针对这两个方面进行优化。但目前大部分工作仅关注其中一个方面,并且几乎所有的模型在进行特征组合时都没有对特征进行区分,同一个特征与其他特征组合时都使用相同的嵌入和组合方法,阻碍了模型性能的提升。为解决该问题,提出了Auto-SEI(Automatic Super-field-aware Feature Embedding and Interacting)模型。该模型先将每个特征子域分配给一个特征超域,再根据分组得到特征的嵌入,然后为特征对选择合适的组合方法获取组合特征,最后进行预测。Auto-SEI模型中,特征子域的划分和组合方法的选择被参数化为架构搜索问题,利用神经架构搜索(Neural Architecture Search,NAS)算法压缩搜索空间并进行选择。在3个真实的大规模数据集上进行了大量实验,结果表明Auto-SEI 模型在点击率预测任务上具有优秀的性能。
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