Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

TL;DR

This paper introduces PINCER, a multi-modal transformer that leverages click-stream data and product catalogs to accurately infer user purchase intent from limited queries in e-commerce, improving retrieval performance.

Contribution

The paper presents a novel multi-modal transformer architecture that integrates external data sources to better understand user intent and enhance query-to-product transformation in e-commerce.

Findings

Outperforms state-of-the-art methods in online retrieval tasks.

Effective in inferring purchase intent from limited user queries.

Ablation studies validate the importance of data integration and architecture design.

Abstract

E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations.