Recommendations Module
Intelligent recommendation system for personalized user experiences. Provides advanced algorithms, real-time personalization, and multi-channel delivery for enhanced user engagement and business growth.
Recommended For
AI Algorithms
Data Sources
Real-Time
The Recommendations module powers intelligent suggestion systems that enhance user experience and business performance. It leverages advanced machine learning algorithms to deliver personalized recommendations across multiple channels and touchpoints.
Advanced Algorithms
Multiple recommendation algorithms including collaborative filtering, content-based, and deep learning approaches
Real-Time Personalization
Dynamic recommendations that adapt to user behavior in real-time across multiple touchpoints
Multi-Source Data Integration
Combine purchase history, browsing behavior, ratings, demographics, and social signals for better recommendations
Cross-Platform Delivery
Serve recommendations through homepage, product pages, email, push notifications, and API endpoints
A/B Testing Framework
Built-in experimentation capabilities to test and optimize recommendation strategies
Diverse Recommendation Types
Support for products, content, users, services, courses, events, and location-based recommendations
Product recommendations for better customer experience
Boost sales with personalized product suggestions and cross-selling
Multi-vendor product and seller recommendations
Help buyers discover relevant products across multiple vendors
Content recommendations for user engagement
Increase watch time with personalized movie, show, and music suggestions
Course and learning path recommendations
Guide learners to relevant courses based on skills and interests
Friend and content recommendations
Connect users with relevant people and increase platform engagement
Match recommendations for user connections
Improve match quality and user satisfaction with intelligent matching
Personalized news and article recommendations
Keep readers engaged with relevant stories and topics
Related content and author recommendations
Increase page views and reader engagement with relevant content
Workout and nutrition recommendations
Personalize fitness journeys with tailored exercise and diet plans
User-based and item-based filtering using user interaction patterns and similarities
Recommendations based on item features and user preference profiles
Combination of multiple algorithms for improved accuracy and coverage
Advanced mathematical techniques for latent factor discovery
Neural networks and deep learning models for complex pattern recognition
Domain expertise and rule-based recommendations for specific use cases
purchase_historyPast transactions and buying patterns for preference modeling
browsing_behaviorPage views, time spent, and navigation patterns
ratingsUser ratings and feedback on products or content
reviewsText analysis of user reviews and comments
demographicsAge, location, gender, and other demographic factors
social_signalsSocial media interactions and network connections
external_dataThird-party data sources and market trends
Featured recommendations on main landing page
Related items and cross-selling suggestions
Personalized email recommendations and newsletters
Mobile and web push notifications with recommendations
RESTful API for custom integrations and third-party apps
Customizable recommendation widgets for any page
trending_itemsPopular and trending items based on community activity
similar_itemsItems similar to those currently being viewed or purchased
frequently_bought_togetherBundle recommendations based on purchase patterns
recently_viewedUser's recent browsing history and continuation suggestions
seasonal_recommendationsTime-aware recommendations based on seasons and events
a_b_testingExperimental framework for testing recommendation strategies
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Recommendations Module",
"description": "Module for recommendation systems and personalized content delivery",
"aimlVersion": "2.0.1",
"schemaVersion": "2.0.1",
"type": "object",
"properties": {
"version": {
"type": "string",
"const": "2.0.1",
"description": "Module version"
},
"enabled": {
"type": "boolean",
"description": "Whether recommendations module is enabled"
},
"algorithms": {
"type": "array",
"items": {
"type": "string",
"enum": ["collaborative_filtering", "content_based", "hybrid", "matrix_factorization", "deep_learning", "knowledge_based"]
},
"description": "Recommendation algorithms in use"
},
"recommendation_types": {
"type": "array",
"items": {
"type": "string",
"enum": ["products", "content", "users", "services", "courses", "events", "locations"]
},
"description": "Types of items being recommended"
},
"personalization": {
"type": "object",
"properties": {
"user_based": { "type": "boolean" },
"behavior_tracking": { "type": "boolean" },
"real_time": { "type": "boolean" },
"cross_platform": { "type": "boolean" }
},
"description": "Personalization features"
},
"data_sources": {
"type": "array",
"items": {
"type": "string",
"enum": ["purchase_history", "browsing_behavior", "ratings", "reviews", "demographics", "social_signals", "external_data"]
},
"description": "Data sources for recommendations"
},
"delivery_channels": {
"type": "array",
"items": {
"type": "string",
"enum": ["homepage", "product_pages", "email", "push_notifications", "api", "widget"]
},
"description": "Where recommendations are displayed"
},
"features": {
"type": "object",
"properties": {
"trending_items": { "type": "boolean" },
"similar_items": { "type": "boolean" },
"frequently_bought_together": { "type": "boolean" },
"recently_viewed": { "type": "boolean" },
"seasonal_recommendations": { "type": "boolean" },
"a_b_testing": { "type": "boolean" }
},
"description": "Recommendation features and capabilities"
}
},
"required": ["version", "enabled"]
}{
"modules": {
"recommendations": {
"version": "2.0.1",
"enabled": true,
"algorithms": [
"collaborative_filtering",
"content_based",
"hybrid",
"deep_learning"
],
"recommendation_types": [
"products",
"content",
"users"
],
"personalization": {
"user_based": true,
"behavior_tracking": true,
"real_time": true,
"cross_platform": true
},
"data_sources": [
"purchase_history",
"browsing_behavior",
"ratings",
"reviews",
"demographics"
],
"delivery_channels": [
"homepage",
"product_pages",
"email",
"push_notifications",
"api"
],
"features": {
"trending_items": true,
"similar_items": true,
"frequently_bought_together": true,
"recently_viewed": true,
"seasonal_recommendations": true,
"a_b_testing": true
}
}
}
}https://schemas.meta-aiml.org/v2.0.1/templates/module/recommendations.jsonIncluded in entity schemas via the modules object