  Visual search plays an essential role for E-commerce. To meet the search
demands of users and promote shopping experience at Alibaba, visual search
relevance of real-shot images is becoming the bottleneck. Traditional visual
search paradigm is usually based upon supervised learning with labeled data.
However, large-scale categorical labels are required with expensive human
annotations, which limits its applicability and also usually fails in
distinguishing the real-shot images. In this paper, we propose to discover
Virtual ID from user click behavior to improve visual search relevance at
Alibaba. As a totally click-data driven approach, we collect various types of
click data for training deep networks without any human annotations at all. In
particular, Virtual ID are learned as classification supervision with co-click
embedding, which explores image relationship from user co-click behaviors to
guide category prediction and feature learning. Concretely, we deploy Virtual
ID Category Network by integrating first-clicks and switch-clicks as
regularizer. Incorporating triplets and list constraints, Virtual ID Feature
Network is trained in a joint classification and ranking manner. Benefiting
from exploration of user click data, our networks are more effective to encode
richer supervision and better distinguish real-shot images in terms of category
and feature. To validate our method for visual search relevance, we conduct an
extensive set of offline and online experiments on the collected real-shot
images. We consistently achieve better experimental results across all
components, compared with alternative and state-of-the-art methods.
