  Text relevance or text matching of query and product is an essential
technique for the e-commerce search system to ensure that the displayed
products can match the intent of the query. Many studies focus on improving the
performance of the relevance model in search system. Recently, pre-trained
language models like BERT have achieved promising performance on the text
relevance task. While these models perform well on the offline test dataset,
there are still obstacles to deploy the pre-trained language model to the
online system as their high latency. The two-tower model is extensively
employed in industrial scenarios, owing to its ability to harmonize performance
with computational efficiency. Regrettably, such models present an opaque
``black box'' nature, which prevents developers from making special
optimizations. In this paper, we raise deep Bag-of-Words (DeepBoW) model, an
efficient and interpretable relevance architecture for Chinese e-commerce. Our
approach proposes to encode the query and the product into the sparse BoW
representation, which is a set of word-weight pairs. The weight means the
important or the relevant score between the corresponding word and the raw
text. The relevance score is measured by the accumulation of the matched word
between the sparse BoW representation of the query and the product. Compared to
popular dense distributed representation that usually suffers from the drawback
of black-box, the most advantage of the proposed representation model is highly
explainable and interventionable, which is a superior advantage to the
deployment and operation of online search engines. Moreover, the online
efficiency of the proposed model is even better than the most efficient inner
product form of dense representation ...
