​ Hey everyone! I've been working on BasketBoost, a Shopify app designed to make product recommendations more relevant instead of just showing random “You may also like” products. The idea is to bring several recommendation experiences together: - 👕 Similar Items: Find genuinely similar alternatives using product images, descriptions, categories, colors and other attributes. - 👗 Complete the Look: Recommend complementary products to build entire outfits, including shoes, trousers, jackets and accessories. - 🛒 Cross-selling: Suggest relevant add-ons on product pages and inside shopping carts. - 🛍️ Frequently Bought Together: Help customers discover products that make sense to purchase together. - 🧠 Smarter ranking: Combine product similarity, merchandising rules and recommendation models instead of relying entirely on generic recommendations. - ⚙️ Merchant controls: Give stores more control over recommendation placements and product relevance. Automatic A B testings filtering of products with no stock in the recomendations, adding things like sales velocity of last 30 90 days to boost specific items. Automatically penalize products with high return rate from th recos. What I've been working on technically: I've been experimenting with visual AI models for fashion similarity, product filtering, learned recommendation ranking and a deterministic fallback system. I'm also working on the Shopify storefront integration. The app is still in development. I'm testing recommendation quality and working toward private merchant testing, rather than claiming improvements in conversion rates before measuring them. I'd love some feedback: If you run a Shopify store, which would be most valuable to you — similar products, complete-the-look outfits, frequently bought together, or cart cross-sells? And what frustrates you about the recommendation apps that already exist? Happy to hear criticism or talk with merchants interested in early testing!   submitted by   /u/IllustriousGrade7691 [link]   [comments]