MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
This addresses the need for efficient product embeddings in e-commerce to improve tasks like recommendation and similarity, but it is incremental as it builds on existing embedding and multi-task learning methods.
The paper tackles the problem of representing e-commerce products as features for machine learning tasks by proposing MRNet-Product2Vec, a multi-task recurrent neural network that learns dense, low-dimensional embeddings from product titles and diverse signals, achieving performance nearly as good as high-dimensional TF-IDF with less than 3% of its dimensions.
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.