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

Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation