Visual Similarity Product Recommendations
For e-commerce sites with 50K+ products. Recommend visually similar products. 35% increase in AOV, 25% improvement in conversion.
E-commerce retailers, fashion brands, and marketplaces who want to improve product discovery through visual similarity recommendations
Traditional recommendation engines rely on purchase history and categories, missing visual preferences. Shoppers who like a product's look cannot find similar items easily
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Why Mixpeek
35% increase in average order value, 25% improvement in conversion rate, and visual recommendations that capture style preferences text cannot express
Overview
Visual similarity drives purchase decisions, especially in fashion and home goods. This use case shows how Mixpeek powers "shop the look" and similar-item recommendations.
Challenges This Solves
Category Silos
Recommendations limited to same category
Impact: Miss cross-sell opportunities, lower AOV
Attribute Gaps
Not all visual attributes are in product data
Impact: Cannot recommend by style, pattern, aesthetic
New Products
No purchase history for new items
Impact: Cold start problem, new products under-recommended
Subjective Style
Style preferences hard to express in search
Impact: Shoppers cannot find items matching their taste
Implementation Steps
Mixpeek analyzes product images to understand visual attributes (style, color, pattern, shape) and recommends products with similar aesthetics regardless of category
Index Product Catalog
Analyze all product images for visual attributes
import { Mixpeek } from 'mixpeek';const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });// Index product catalog with visual analysisawait client.buckets.connect({collection_id: 'product-catalog',bucket_uri: 's3://catalog/images/',extractors: ['image-embedding', // Visual similarity'color-extraction', // Dominant colors, palette'pattern-detection', // Stripes, florals, solids'style-classification', // Modern, vintage, casual, formal'object-detection' // Product type, components],settings: {product_id_field: 'sku',multiple_images_per_product: true,extract_attributes: ['color_family', 'pattern', 'style', 'material_appearance','shape', 'complexity', 'aesthetic']}});
Get Visual Recommendations
Find visually similar products
// Get visually similar product recommendationsasync function getVisualRecommendations(productId: string, options?: {limit?: number;same_category?: boolean;price_range?: { min: number; max: number };exclude_owned?: string[];}) {const product = await client.collections.get(productId);const similar = await client.retrieve({collection_id: 'product-catalog',query: {type: 'image',embedding: product.image_embedding},filters: {sku: { $ne: productId },in_stock: true,category: options?.same_category ? product.category : undefined,price: options?.price_range ? {$gte: options.price_range.min,$lte: options.price_range.max} : undefined},return_fields: ['sku', 'name', 'price', 'image_url','category', 'similarity_score', 'visual_attributes'],limit: options?.limit ?? 12});return similar.map(p => ({...p,similarity_reason: explainSimilarity(product, p)}));}
Power Shop-the-Look
Find complementary products for complete looks
// Get complementary products for "shop the look"async function getComplementaryProducts(productId: string, lookType: string) {const product = await client.collections.get(productId);// Find products that complement visually (same style, coordinating colors)const complementary = await client.recommendations.getComplementary({product_id: productId,product_embedding: product.image_embedding,product_attributes: product.visual_attributes,look_type: lookType, // 'outfit', 'room', 'collection'rules: [{ attribute: 'style', match: 'same' },{ attribute: 'color_family', match: 'complementary' },{ attribute: 'category', match: 'different' } // Cross-category],limit: 6});return {anchor_product: product,complementary_items: complementary,look_total: product.price + complementary.reduce((sum, p) => sum + p.price, 0)};}
Personalize by Visual Preference
Learn shopper visual preferences
// Build visual preference profile from behaviorasync function updateVisualPreferences(userId: string, interaction: {product_id: string;action: 'view' | 'save' | 'cart' | 'purchase';}) {const product = await client.collections.get(interaction.product_id);// Weight by action strengthconst weight = {view: 0.1,save: 0.5,cart: 0.7,purchase: 1.0}[interaction.action];await client.users.updatePreferences({user_id: userId,visual_signals: {embedding: product.image_embedding,attributes: product.visual_attributes,weight: weight}});}// Get personalized visual recommendationsasync function getPersonalizedRecommendations(userId: string) {return client.retrieve({collection_id: 'product-catalog',query: {type: 'user_preferences',user_id: userId},filters: { in_stock: true },limit: 20});}
Expected Outcomes
35% increase from visual cross-sell
Average Order Value
25% improvement on recommendation clicks
Conversion Rate
50% more catalog explored per session
Product Discovery
3x faster sales ramp for new products
New Product Performance
15% reduction (better style matching)
Return Rate
Frequently Asked Questions
Related Resources
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