Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou, Chengru Song, Xiaoqiang Zhu Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, Kun Gai Alibaba Group guorui.xgr, chengru.scr, xiaoqiang.zxq, zhuhan.zh, fanying.fy, maxiao.ma, yanghui.yyh, junqi.jjq,
Abstract
Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user’s diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two tech
中文速览
广告点击率预测长期依赖"嵌入向量+多层感知机"的固定长度用户表示方式，但这种做法把用户所有兴趣压缩进同一个向量，无法随候选广告的不同而动态调整，严重限制了模型对用户多样兴趣的表达能力。阿里巴巴团队提出了深度兴趣网络（Deep Interest Network，DIN），核心思路是在预测某条广告时，用一个局部激活单元（local activation unit）对用户历史行为做加权求和，与当前候选广告越相关的历史行为权重越高，从而让用户的兴趣表示向量随广告不同而自适应变化。此外，为解决亿级参数工业网络难以训练的问题，他们还提出了小批量感知正则化和数据自适应激活函数两项训练技巧。在两个公开数据集和包含逾20亿样本的阿里真实生产数据集上，DIN均超越了当时的主流方法，目前已成功部署在阿里巴巴展示广告系统的主流量服务中，为广告收入带来了显著提升。
原文 arXiv:1706.06978;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1706.06978v4