【摘要】 探索大规模稀疏数据背景下的交叉特征,对提高推荐系统的点击率预测精度十分重要,现有方法往往通过显式暴力枚举或隐式DNN提取完成特征交叉,其掺杂了大量无用、冗余特征,极大地限制了点击率预测模型的表现。对此提出一种显式和隐式相结合的新型特征交叉网络(Deep&Attention Cross Network, DACN)。通过引入基于注意力机制的动态交叉网络,实现以线性空间复杂度完成指定阶显式特征组合,同时消除无用、冗余特征带来的影响。经过实验验证,DACN在预测准确性和参数占用方面相较于现有方法都有提升。更多还原

【Abstract】 Exploring the combinatorial features in the context of large-scale sparse data are essential for improving the accuracy of click through rate prediction in recommendation system. The existing methods usually use explicit violence enumeration or implicit DNN extraction to complete feature combination, which are mixed with a large number of useless and redundant features, and greatly limits the performance of CTR model. This paper proposes a new explicit and implicit network architecture deep & attention cross network(DACN). It introduced a dynamic cross network based on attention mechanism to realize the combination of specified order explicit features with linear space complexity, and eliminated the influence of useless and redundant features. Experimental results show that DACN is better than the existing methods in prediction accuracy and parameter occupancy.更多还原