Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context
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arXiv
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| Format: | Preprint |
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2025
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| author | Kekuda, Akshay Dandu, Murali Mohana Krishna Lahiri, Rimita Cai, Shiqin Subramaniam, Sinduja Korpeoglu, Evren Achan, Kannan |
| author_facet | Kekuda, Akshay Dandu, Murali Mohana Krishna Lahiri, Rimita Cai, Shiqin Subramaniam, Sinduja Korpeoglu, Evren Achan, Kannan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context Kekuda, Akshay Dandu, Murali Mohana Krishna Lahiri, Rimita Cai, Shiqin Subramaniam, Sinduja Korpeoglu, Evren Achan, Kannan Information Retrieval Artificial Intelligence 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. |
| title | Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.02890 |