/** * E-Commerce Recommendations: Personalized Product Discovery * * Use Case: Recommend similar products based on user preferences, * browsing history, and product embeddings. * * Optimization Priority: DIVERSITY + RELEVANCE */ import { UnifiedMetrics } from '../../types'; export declare const ECOMMERCE_ATTENTION_CONFIG: { heads: number; forwardPassTargetMs: number; batchSize: number; precision: "float32"; diversityBoost: boolean; clustering: { algorithm: "louvain"; minModularity: number; semanticPurity: number; hierarchicalLevels: number; }; dynamicK: { min: number; max: number; adaptationStrategy: "user-engagement"; }; }; export interface ECommerceMetrics extends UnifiedMetrics { clickThroughRate: number; conversionRate: number; diversityScore: number; categoryBalanceScore: number; userSatisfaction: number; } export interface Recommendation { productId: string; relevanceScore: number; category: string; cluster: string; priceUSD: number; } export declare function recommendProducts(userProfile: Float32Array, // User preferences embeddings productCatalog: any, // HNSWGraph type userEngagement: number, applyAttention: (data: Float32Array, config: any) => Promise, applyDiversityBoost: (candidates: any[], weight: number) => Promise, clusterRecommendations: (items: any[], config: any) => Promise, findCluster: (item: any, clusters: any[]) => string, diversityWeight?: number): Promise; export declare const ECOMMERCE_PERFORMANCE_TARGETS: { p95LatencyMs: number; clickThroughRate: number; conversionRate: number; diversityScore: number; uptimePercent: number; }; export declare const ECOMMERCE_CONFIG_VARIATIONS: { fashion: { heads: number; diversityBoost: boolean; clustering: { hierarchicalLevels: number; algorithm: "louvain"; minModularity: number; semanticPurity: number; }; forwardPassTargetMs: number; batchSize: number; precision: "float32"; dynamicK: { min: number; max: number; adaptationStrategy: "user-engagement"; }; }; electronics: { heads: number; specificationWeight: number; diversityBoost: boolean; forwardPassTargetMs: number; batchSize: number; precision: "float32"; clustering: { algorithm: "louvain"; minModularity: number; semanticPurity: number; hierarchicalLevels: number; }; dynamicK: { min: number; max: number; adaptationStrategy: "user-engagement"; }; }; grocery: { heads: number; forwardPassTargetMs: number; batchSize: number; dynamicK: { min: number; max: number; adaptationStrategy: "cart-size"; }; precision: "float32"; diversityBoost: boolean; clustering: { algorithm: "louvain"; minModularity: number; semanticPurity: number; hierarchicalLevels: number; }; }; luxury: { heads: number; forwardPassTargetMs: number; diversityBoost: boolean; precision: "float32"; batchSize: number; clustering: { algorithm: "louvain"; minModularity: number; semanticPurity: number; hierarchicalLevels: number; }; dynamicK: { min: number; max: number; adaptationStrategy: "user-engagement"; }; }; }; export declare function adaptConfigToUserSegment(baseConfig: typeof ECOMMERCE_ATTENTION_CONFIG, segment: 'browser' | 'buyer' | 'loyal' | 'vip'): typeof ECOMMERCE_ATTENTION_CONFIG; export interface PromotionalContext { isSale: boolean; seasonalEvent: string | null; inventoryPressure: number; } export declare function adaptConfigToPromotion(baseConfig: typeof ECOMMERCE_ATTENTION_CONFIG, context: PromotionalContext): typeof ECOMMERCE_ATTENTION_CONFIG; export declare function generateABTestConfigs(baseConfig: typeof ECOMMERCE_ATTENTION_CONFIG): Record; //# sourceMappingURL=e-commerce-recommendations.d.ts.map