Feature Dual Supervision Model for the Searches of Online Advertising Audiences

Abstract

Online advertising has become one of the most important strategies used by companies. They get the valuable results from Internet marketing and communication strategies. Therefore, it is necessary to study the click-through rate (CTR) model to search the potential audiences in online advertising. The advertisers desire to search for potential candidates through a large number of queries for audiences in programmatic advertising. Facing such a large corpus, the most common method is that using two-tower model to learn user's queries and ad representations, and then the similarity function is applied to match the feature representation to get the potential audiences related to the ad. However, in the process of feature extraction, there is a lack of information interaction between the two towers, resulting in the loss of details in the representation. In order to alleviate the lack of information interaction between the networks in the two-tower model during feature extraction. In this paper, we propose a novel model named Feature Dual Supervision Model (FDSM), which integrates by Feature Expression Unit (FEU) and Feature Supervision Unit (FSU). The FEU is used to extract ads or users features, and FSU generates a weight vector to supervise the working process of the FEU. In addition, we propose a feature cross-layer with bridge connections in FDSM to achieve effective feature interaction between ad and user representations. Finally, we conduct experiments on the Tencent Lookalike and MovieLens datasets. The experimental results indicate that the FDSM model outperforms other state-of-the-art CTR prediction models in audience expansion.