"Target has deployed a generative AI-based accessory recommendation system to address the growing complexity of pairing complementary products across its large retail catalog. Developed by the Product Recommendations team, GRAM, a GenAI-based Related Accessory Model for the Home category, uses large language models to identify which product attributes matter most when recommending related accessories, helping shoppers find items that go well together."
"At the core of GRAM's design is the use of large language models to analyze structured product data. The model evaluates which attributes are most significant for each core-accessory pairing, assigns weights accordingly, and generates relevance scores that determine which items are presented to shoppers. For example, when recommending a throw pillow to go with a sofa, the system might emphasize color and material, while suggesting a battery for a toy prioritizes compatibility and kid-safety features."
GRAM is a GenAI-based Related Accessory Model deployed to recommend complementary products across a large retail catalog. The system uses large language models to analyze structured product attributes, determine which attributes matter for specific core-accessory pairings, assign weights, and produce relevance scores to rank accessory suggestions. The approach automates attribute-importance assessment across categories, reducing manual curation and scaling recommendation coverage. The model emphasizes different attributes depending on product type, such as color and material for home decor or compatibility and safety for toys. Merchant-curated lists and human-in-the-loop review ensure business relevance and seasonal or cross-category guidance.
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