Enhanced Video BERT for Fast Video Advertisement Retrieval.

Recently, video BERT based on cross-modal attention has achieved excellent performance in many cross-modal tasks in academia. Nevertheless, the expensive computation cost of cross-modal attention makes video BERT impractical for large-scale search in industrial applications. Inspired by the success of the tree-based deep model (TDM) in the recommendation system, we present a enhanced video BERT (EVB). It provides a practical solution to deploy the heavy video BERT for the large-scale query-to-video search. The proposed EVB overcomes the limitation of TDM relying on global features, and makes the tree structure based on a global feature compatible with version BERT using a set of local features. What’s more, we proposes a similarity-based dynamic construction to integrate the optimization of model efficiency. The proposed EVB has been deployed in our video advertising platform and brings a considerable boost in CVR and CTR for advertisers.