  With the advancement of multimedia internet, the impact of visual
characteristics on the decision of users to click or not within the online
retail industry is increasingly significant. Thus, incorporating visual
features is a promising direction for further performance improvements in
click-through rate (CTR). However, experiments on our production system
revealed that simply injecting the image embeddings trained with established
pre-training methods only has marginal improvements. We believe that the main
advantage of existing image feature pre-training methods lies in their
effectiveness for cross-modal predictions. However, this differs significantly
from the task of CTR prediction in recommendation systems. In recommendation
systems, other modalities of information (such as text) can be directly used as
features in downstream models. Even if the performance of cross-modal
prediction tasks is excellent, it is challenging to provide significant
information gain for the downstream models. We argue that a visual feature
pre-training method tailored for recommendation is necessary for further
improvements beyond existing modality features. To this end, we propose an
effective user intention reconstruction module to mine visual features related
to user interests from behavior histories, which constructs a many-to-one
correspondence. We further propose a contrastive training method to learn the
user intentions and prevent the collapse of embedding vectors. We conduct
extensive experimental evaluations on public datasets and our production system
to verify that our method can learn users' visual interests. Our method
achieves $0.46\%$ improvement in offline AUC and $0.88\%$ improvement in Taobao
GMV (Cross Merchandise Volume) with p-value$<$0.01.
