  E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
billions of products. Machine learning (ML) algorithms involving products are
often used to improve the customer experience and increase revenue, e.g.,
product similarity, recommendation, and price estimation. The products are
required to be represented as features before training an ML algorithm. In this
paper, we propose an approach called MRNet-Product2Vec for creating generic
embeddings of products within an e-commerce ecosystem. We learn a dense and
low-dimensional embedding where a diverse set of signals related to a product
are explicitly injected into its representation. We train a Discriminative
Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a
product title fed through a Bidirectional RNN and at the output, product labels
corresponding to fifteen different tasks are predicted. The task set includes
several intrinsic characteristics about a product such as price, weight, size,
color, popularity, and material. We evaluate the proposed embedding
quantitatively and qualitatively. We demonstrate that they are almost as good
as sparse and extremely high-dimensional TF-IDF representation in spite of
having less than 3% of the TF-IDF dimension. We also use a multimodal
autoencoder for comparing products from different language-regions and show
preliminary yet promising qualitative results.
