Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context
It addresses a critical problem for e-commerce platforms by improving cross-category recommendations, though it is incremental in applying existing methods like LLMs and transformers to a specific domain.
This paper tackles the challenge of recommending general merchandise to grocery shoppers by introducing a cross-pollination framework that uses LLMs and real-time cart context, resulting in a 36% increase in add-to-cart rate with LLM-based retrieval and a 15% lift with cart context-based ranking.
Modern e-commerce platforms strive to enhance customer experience by providing timely and contextually relevant recommendations. However, recommending general merchandise to customers focused on grocery shopping -- such as pairing milk with a milk frother -- remains a critical yet under-explored challenge. This paper introduces a cross-pollination (XP) framework, a novel approach that bridges grocery and general merchandise cross-category recommendations by leveraging multi-source product associations and real-time cart context. Our solution employs a two-stage framework: (1) A candidate generation mechanism that uses co-purchase market basket analysis and LLM-based approach to identify novel item-item associations; and (2) a transformer-based ranker that leverages the real-time sequential cart context and optimizes for engagement signals such as add-to-carts. Offline analysis and online A/B tests show an increase of 36\% add-to-cart rate with LLM-based retrieval on the item page, and 15\% lift in add-to-cart using cart context-based ranker on the cart page. Our work contributes practical techniques for cross-category recommendations and broader insights for e-commerce systems.