图 书 馆
深度和跨网络广告点击预测Deep & Cross Network for Ad Click Predictions |
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| 课程网址: | http://videolectures.net/kdd2017_wang_click_predictions/ |
| 主讲教师: | Ruoxi Wang |
| 开课单位: | 斯坦福大学计算与数学工程研究所 |
| 开课时间: | 2017-12-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Feature engineering has been the key to the success of many prediction models. However, the process is nontrivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage. |
| 关 键 词: | 交叉网络; 模型预测; 特征工程 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-20：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 34 |