MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings

TL;DR

This paper introduces MRNet-Product2Vec, a multi-task RNN-based approach for creating compact, informative product embeddings that capture diverse product features for improved e-commerce machine learning applications.

Contribution

It presents a novel multi-task bidirectional RNN model that generates low-dimensional product embeddings incorporating multiple product attributes.

Findings

Embeddings are nearly as effective as high-dimensional TF-IDF representations.

The approach reduces dimensionality to less than 3% of TF-IDF while retaining performance.

Preliminary results show promising cross-language product comparison capabilities.

Abstract

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.