  Extracting expressive visual features is crucial for accurate
Click-Through-Rate (CTR) prediction in visual search advertising systems.
Current commercial systems use off-the-shelf visual encoders to facilitate fast
online service. However, the extracted visual features are coarse-grained
and/or biased. In this paper, we present a visual encoding framework for CTR
prediction to overcome these problems. The framework is based on contrastive
learning which pulls positive pairs closer and pushes negative pairs apart in
the visual feature space. To obtain fine-grained visual features,we present
contrastive learning supervised by click through data to fine-tune the visual
encoder. To reduce sample selection bias, firstly we train the visual encoder
offline by leveraging both unbiased self-supervision and click supervision
signals. Secondly, we incorporate a debiasing network in the online CTR
predictor to adjust the visual features by contrasting high impression items
with selected items with lower impressions.We deploy the framework in the
visual sponsor search system at Alibaba. Offline experiments on billion-scale
datasets and online experiments demonstrate that the proposed framework can
make accurate and unbiased predictions.
