Recommendations modules provide different types of product recommendations including similar items, frequently bought together, “complete the look”, and personalized picks.

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Overview

Recommendations modules help users discover products through various recommendation strategies, each optimized for different use cases. The Marqo pixel automatically collects user interaction data (product views, purchases, add-to-cart events) to power these recommendations, so they work automatically without requiring any manual data collection.

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Module Types

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Similar Items

Recommend products similar to a viewed product: 
{
  "module_type": "similar_items",
  "seed_product": "prod_123",
  "algorithm": "content_based",
  "max_results": 10,
  "diversity": "medium"
}

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Frequently Bought Together

Recommend products often purchased together: 
{
  "module_type": "frequently_bought_together",
  "seed_product": "prod_123",
  "algorithm": "collaborative_filtering",
  "max_results": 5
}

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Complete the Look

Recommend complementary products: 
{
  "module_type": "complete_the_look",
  "seed_product": "prod_123",
  "complementary_types": ["accessories", "matching_items"],
  "max_results": 8
}

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Personalized Picks

User-specific recommendations: 
{
  "module_type": "personalized_picks",
  "user_id": "user_456",
  "algorithm": "hybrid",
  "max_results": 12
}

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Best Practices

  1. Choose right module: Select modules appropriate for context
  2. Test performance: A/B test different modules
  3. Monitor metrics: Track click-through and conversion rates
  4. Maintain diversity: Ensure recommendations aren’t too narrow
  5. Update regularly: Keep recommendations fresh