標題: SEM: A Softmax-based Ensemble Model for CTR Estimation in Real-Time Bidding Advertising
作者: Zhu, Wen-Yuan
Wang, Chun-Hao
Shih, Wen-Yueh
Peng, Wen-Chih
Huang, Jiun-Long
交大名義發表
National Chiao Tung University
公開日期: 1-Jan-2017
摘要: In Real-Time Bidding (RTB) advertising, evaluating the Click-Through Rate (CTR) of a bid request and an ad is important for bidding strategy optimization on Demand-Side Platforms (DSPs). The regression-based approaches are popular for CTR estimation in RTB since this kind of approach is highly efficient and scalable. The information of the bid request and the ad contains categorical attributes (such URL) and numerical attributes (such ad size). To vectorize the information for the input of regression-based approaches, the categorical attributes will be expanded to several binary features in general. However, some categorical attributes have infinite possible values (such as URL). Thus, for these attributes, only observed values in training will be transformed into binary features. If there is a new attribute or value in online environment, this information will be lost after vectorization. In this paper, we first exploit the feature hashing trick to transform the categorical and numerical attributes into the large fixed size vector. Since the vector is large and sparse, we propose a Softmax-based Ensemble Model, SEM, which adopts only a few key features after feature hashing for CTR estimation. The experimental results demonstrate that our proposed approach is able to adapt to the harsh environments in RTB, and outperforms the state-of-the-art approaches effectively when only less than 50 features are adopted in two real datasets.
URI: http://hdl.handle.net/11536/146625
ISSN: 2375-933X
期刊: 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)
起始頁: 5
結束頁: 12
Appears in Collections:Conferences Paper