TFNet: Multi-Semantic Feature Interaction for CTR Prediction | |
Shu Wu2; Feng Yu2; Xueli Yu2; Qiang Liu2; Liang Wang2; Tieniu Tan2; Jie Shao1; Fan Huang1 | |
2020-07-25 | |
会议日期 | 2020/07/25-30 |
会议地点 | Virtual Event, China |
英文摘要 | The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in this field, such as Logistic Regression (LR), Factorization Machines (FM) and deep learning based methods like Wide&Deep, Neural Factorization Machines (NFM) and DeepFM. However, such approaches generally use the vector-product of each pair of features, which have ignored the different semantic spaces of the feature interactions. In this paper, we propose a novel Tensor-based Feature interaction Network (TFNet) model, which introduces an operating tensor to elaborate feature interactions via multi-slice matrices in multiple semantic spaces. Extensive offline and online experiments show that TFNet: 1) outperforms the competitive compared methods on the typical Criteo and Avazu datasets; 2) achieves large improvement of revenue and click rate in online A/B tests in the largest Chinese App recommender system, Tencent MyApp. |
会议录出版者 | SIGIR ’20 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57495] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
作者单位 | 1.Tencent 2.中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shu Wu,Feng Yu,Xueli Yu,et al. TFNet: Multi-Semantic Feature Interaction for CTR Prediction[C]. 见:. Virtual Event, China. 2020/07/25-30. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论