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TFNet: Multi-Semantic Feature Interaction for CTR Prediction
Shu Wu1; Feng Yu1; Xueli Yu1; Qiang Liu1; Liang Wang1; Tieniu Tan1; Jie Shao2; Fan Huang2
Conference NameProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference Date2020/07/25-30
Conference PlaceVirtual Event, China
PublisherSIGIR ’20

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.

Sub direction classification推荐系统
planning direction of the national heavy laboratory智能计算与学习
Paper associated data
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shu Wu,Feng Yu,Xueli Yu,et al. TFNet: Multi-Semantic Feature Interaction for CTR Prediction[C]:SIGIR ’20,2020.
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