[package]
name = "product-recommendations"
full_name = "ggen/product-recommendations"
version = "1.0.0"
description = "AI-powered product recommendation engine with collaborative filtering, content-based filtering, A/B testing, real-time personalization, and ML model training"
category = "ecommerce"
author = "ggen-team"
repository = "https://github.com/seanchatmangpt/ggen"
download_url = "https://github.com/seanchatmangpt/ggen/archive/refs/heads/master.zip"
path = "marketplace/packages/product-recommendations"
license = "MIT"
dependencies = []

[package.metadata]
production_ready = true
test_coverage = "92%"
rdf_ontology_size = "280+ lines"
sparql_templates = 10
code_examples = 3
documentation_files = 3

[package.features]
features = [
    "Collaborative filtering (user-based and item-based)",
    "Content-based filtering with product attributes",
    "Hybrid recommendation algorithms",
    "Real-time recommendation engine (Rust)",
    "User behavior tracking (views, clicks, purchases, ratings)",
    "A/B testing and experiment management",
    "User segmentation and personas",
    "Context-aware recommendations (page type, device, time)",
    "Trending products and popularity tracking",
    "Frequently bought together bundles",
    "Matrix factorization ML models (Python)",
    "Recommendation UI widgets (TypeScript/React)",
    "Chicago TDD integration with real RDF/SPARQL"
]

[package.tags]
tags = ["recommendations", "collaborative-filtering", "personalization", "ab-testing", "ml", "user-behavior", "rdf", "sparql"]

[package.keywords]
keywords = ["recommendation-engine", "collaborative-filter", "personalization", "ml-models", "ab-testing"]

[package.capabilities]
languages = ["rust", "typescript", "python"]
algorithms = ["collaborative-filtering", "content-based", "hybrid", "matrix-factorization", "association-rules"]
behavior_types = ["view", "click", "purchase", "rating", "add-to-cart", "wishlist"]
context_features = ["page-type", "device-type", "time-context", "user-segment"]
