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FAQs

Answers to help you understand how RecoMelon transforms your product recommendations

Does it replace Shopify's built-in search or just enhance it?

RecoMelon can replace or enhance Shopify's built-in search depending on setup. Its primary function is visual product discovery on PDPs - surfacing similar products, alternative styles, and complementary items. RecoMelon also supports collection-level discovery overlays and "More like this" triggers, giving merchants flexibility over where visual AI recommendations surface across the store.

Can customers filter recommendations themselves?

Shopify customers can interact directly with Dealbreaker™ Filters inside the RecoMelon recommendation panel. By selecting their non-negotiable attributes - neckline, fabric, fit, sleeve length, colour - shoppers refine the recommendations in real time without leaving the product page. This gives customers active control over discovery and reduces the chance they bounce because the recs shown don't match their intent.

Will this work for my vertical?

RecoMelon works across all Shopify product verticals with visual variety. The VisualDNA™ model has been trained across 200+ ecommerce categories including fashion, footwear, accessories, beauty, home décor, fitness, furniture, and jewellery. Any catalogue where products differ visually - by colour, shape, style, or form - benefits from RecoMelon's visual matching. Text-only or commodity product catalogues are less suited.

How does RecoMelon learn about my products?

RecoMelon does not require any purchase data to get started. VisualDNA™ reads the visual structure of each product image - silhouette, fabric texture, colour, print, and cut - to identify visually similar products from day one. Over time, LookLogic™ Behaviour captures engagement signals like time spent on product details, image scrolls, and filter interactions, making recommendations progressively sharper as traffic builds. New stores, new product drops, and low-traffic SKUs are all handled immediately without waiting for sales history.

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