  Discovering the intended items of user queries from a massive repository of
items is one of the main goals of an e-commerce search system. Relevance
prediction is essential to the search system since it helps improve
performance. When online serving a relevance model, the model is required to
perform fast and accurate inference. Currently, the widely used models such as
Bi-encoder and Cross-encoder have their limitations in accuracy or inference
speed respectively. In this work, we propose a novel model called the
Entity-Based Relevance Model (EBRM). We identify the entities contained in an
item and decompose the QI (query-item) relevance problem into multiple QE
(query-entity) relevance problems; we then aggregate their results to form the
QI prediction using a soft logic formulation. The decomposition allows us to
use a Cross-encoder QE relevance module for high accuracy as well as cache QE
predictions for fast online inference. Utilizing soft logic makes the
prediction procedure interpretable and intervenable. We also show that
pretraining the QE module with auto-generated QE data from user logs can
further improve the overall performance. The proposed method is evaluated on
labeled data from e-commerce websites. Empirical results show that it achieves
promising improvements with computation efficiency.
