Query2Prod2Vec

Categories: Business & Sales, Data Analysis, Research | Pricing: Free | Official Website ↗

Query2Prod2Vec is an AI model that grounds lexical representations for product search in product embeddings, mapping words to a latent space of products.

Query2Prod2Vec is a model designed to improve product search in e-commerce by grounding lexical representations in product embeddings. It establishes meaning as a mapping between words and a latent space of products within a digital shop. The model leverages shopping sessions to learn this underlying product space. The system uses merchandising annotations to construct lexical analogies for evaluation, demonstrating higher accuracy compared to existing techniques in Natural Language Processing (NLP) and Information Retrieval (IR) literature. It emphasizes data efficiency, making it suitable for product search applications beyond large retail enterprises and addressing practical constraints faced by many practitioners.

Key Features

grounds lexical representations for product search
maps words to a latent space of products
learns underlying product space from shopping sessions
uses merchandising annotations for evaluation
demonstrates higher accuracy than traditional NLP/IR techniques
designed for data efficiency

Pros

improves accuracy of product search
grounds word meanings in product context
efficient with data, suitable for non-giant retailers
outperforms known NLP and IR techniques
addresses practical constraints for practitioners

Cons

this is a research paper, not a commercial product
implementation details not provided for direct use
requires shopping session data for learning
evaluation relies on merchandising annotations
no direct user interface or API mentioned

Use Cases

enhancing product search relevance in online stores
improving product recommendation systems
understanding customer query intent in e-commerce
developing more accurate lexical representations for products

Best For

e-commerce researchers
data scientists in retail
developers building product search engines
companies with limited data resources

Watch demo on YouTube ↗

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