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Product embedding serves as a cornerstone for a wide range of applications ineCommerce. The product embedding learned from multiple modalities showssignificant improvement over that from a single modality, since differentmodalities provide complementary information. However, some modalities are moreinformatively dominant than others. How to teach a model to learn embeddingfrom different modalities without neglecting information from the less dominantmodality is challenging. We present an image-text embedding model (ITEm), anunsupervised learning method that is designed to better attend to image andtext modalities. We extend BERT by (1) learning an embedding from text andimage without knowing the regions of interest; (2) training a globalrepresentation to predict masked words and to construct masked image patcheswithout their individual representations. We evaluate the pre-trained ITEm ontwo tasks: the search for extremely similar products and the prediction ofproduct categories, showing substantial gains compared to strong baselinemodels.

ITEm: Unsupervised Image-Text Embedding Learning for eCommerce