  Scene marketing that well demonstrates user interests within a certain
scenario has proved effective for offline shopping. To conduct scene marketing
for e-commerce platforms, this work presents a novel product form, scene-based
topic channel which typically consists of a list of diverse products belonging
to the same usage scenario and a topic title that describes the scenario with
marketing words. As manual construction of channels is time-consuming due to
billions of products as well as dynamic and diverse customers' interests, it is
necessary to leverage AI techniques to automatically construct channels for
certain usage scenarios and even discover novel topics. To be specific, we
first frame the channel construction task as a two-step problem, i.e.,
scene-based topic generation and product clustering, and propose an E-commerce
Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated
production, consisting of scene-based topic generation model for the e-commerce
domain, product clustering on the basis of topic similarity, as well as quality
control based on automatic model filtering and human screening. Extensive
offline experiments and online A/B test validates the effectiveness of such a
novel product form as well as the proposed system. In addition, we also
introduce the experience of deploying the proposed system on a real-world
e-commerce recommendation platform.
