  In this paper, we introduce Cross-modal Alignment with mixture experts Neural
Network (CameNN) recommendation model for intral-city retail industry, which
aims to provide fresh foods and groceries retailing within 5 hours delivery
service arising for the outbreak of Coronavirus disease (COVID-19) pandemic
around the world. We propose CameNN, which is a multi-task model with three
tasks including Image to Text Alignment (ITA) task, Text to Image Alignment
(TIA) task and CVR prediction task. We use pre-trained BERT to generate the
text embedding and pre-trained InceptionV4 to generate image patch embedding
(each image is split into small patches with the same pixels and treat each
patch as an image token). Softmax gating networks follow to learn the weight of
each transformer expert output and choose only a subset of experts conditioned
on the input. Then transformer encoder is applied as the share-bottom layer to
learn all input features' shared interaction. Next, mixture of transformer
experts (MoE) layer is implemented to model different aspects of tasks. At top
of the MoE layer, we deploy a transformer layer for each task as task tower to
learn task-specific information. On the real word intra-city dataset,
experiments demonstrate CameNN outperform baselines and achieve significant
improvements on the image and text representation. In practice, we applied
CameNN on CVR prediction in our intra-city recommender system which is one of
the leading intra-city platforms operated in China.
