A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Scenario in WeChat.

Click-through rate (CTR) estimation plays as a pivotal function module in various online services. Previous studies mainly apply CTR models to the field of recommendation or online advertisement. Indeed, CTR is also critical in information retrieval, since the CTR probability can serve as a valuable feature for a query-document pair. In this paper, we study the CTR task under account search scenario in WeChat, where users search official accounts or mini programs corresponding to an organization. Despite the large number of CTR models, directly applying them to our task is inappropriate since the account retrieval task has a number of specific characteristics. E.g., different from traditional user-centric CTR models, in our task, CTR prediction is query-centric and does not model user information. In addition, queries and accounts are short texts, and heavily rely on prior knowledge and semantic understanding. These characteristics require us to specially design a CTR model for the task. To this end, we propose a novel CTR prediction model named Knowledge eNhanced hIerarchical Fusion nEtwork (KNIFE). Specifically, to tackle the prior information problem, we mine the knowledge graph of accounts as side information; to enhance the representations of queries, we construct a bipartite graph for queries and accounts. In addition, a hierarchical network structure is proposed to fuse the representations of different information in a fine-grained manner. Finally, the representations of queries and accounts are obtained from this hierarchical network and fed into the CTR model together with other features for prediction. We conduct extensive experiments against 12 existing models across two industrial datasets. Both offline and online A/B test results indicate the effectiveness of KNIFE.