EGM: Enhanced Graph-based Model for Large-scale Video Advertisement Search

Video advertisements may grasp customers' attention instantly and are often adored by advertisers. Since the corpus is vast, achieving an efficient query-to-video search can be challenging. Because traditional approximate nearest neighborhood (ANN) search methods are based simple similarities (e.g., cosine or inner products) on embedding vectors. They are often not sufficient for bridging the modal gap between a text query and video advertisements and typically can only achieve sub-optimal performance in query-to-video search. Tree-based deep model (TDM) overcomes the limited matching capability of embedding-based methods but suffers from the data sparsity problem. Deep retrieval model adopts a graph-based model which overcomes the data sparsity problem in TDM by sharing the nodes. But the shared nodes entangle features of different items, making it difficult to distinguish similar items. In this work, we enhance the graph-based model through sub-path embedding to differentiate similar videos. The added sub-path embedding provides personalized characteristics, beneficial for modeling fine-grain details to discriminate similar items. After launching enhanced graph model (EGM), the click-through rate (CTR) relatively increases by 1.33%, and the conversion rate (CVR) relatively by 1.07%.