  Click-through-rate (CTR) prediction has an essential impact on improving user
experience and revenue in e-commerce search. With the development of deep
learning, graph-based methods are well exploited to utilize graph structure
extracted from user behaviors and other information to help embedding learning.
However, most of the previous graph-based methods mainly focus on
recommendation scenarios, and therefore their graph structures highly depend on
item's sequential information from user behaviors, ignoring query's sequential
signal and query-item correlation. In this paper, we propose a new approach
named Light-weight End-to-End Graph Interest Network (EGIN) to effectively mine
users' search interests and tackle previous challenges. (i) EGIN utilizes query
and item's correlation and sequential information from the search system to
build a heterogeneous graph for better CTR prediction in e-commerce search.
(ii) EGIN's graph embedding learning shares the same training input and is
jointly trained with CTR prediction, making the end-to-end framework effortless
to deploy in large-scale search systems. The proposed EGIN is composed of three
parts: query-item heterogeneous graph, light-weight graph sampling, and
multi-interest network. The query-item heterogeneous graph captures correlation
and sequential information of query and item efficiently by the proposed
light-weight graph sampling. The multi-interest network is well designed to
utilize graph embedding to capture various similarity relationships between
query and item to enhance the final CTR prediction. We conduct extensive
experiments on both public and industrial datasets to demonstrate the
effectiveness of the proposed EGIN. At the same time, the training cost of
graph learning is relatively low compared with the main CTR prediction task,
ensuring efficiency in practical applications.
