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Intent Handling

Intent handling detects what users are looking for in their queries (brand, category, attribute, use-case) and applies appropriate ranking behavior for each intent type.

Overview​

Different query types require different ranking strategies. Intent handling automatically detects query intent and applies the most appropriate ranking behavior, ensuring users find what they're looking for.

Intent Types​

Brand Intent​

Users searching for a specific brand:

  • Queries: "Nike shoes", "Apple iPhone", "Samsung TV"
  • Strategy: Prioritize exact brand matches, show brand-specific products
  • Ranking: High weight on brand match, lower weight on other attributes
{
  "intent_type": "brand",
  "detection_patterns": [
    "brand_name + product_type",
    "brand_name only"
  ],
  "ranking_strategy": {
    "brand_match_weight": 0.8,
    "relevance_weight": 0.2
  }
}

Category Intent​

Users browsing a category:

  • Queries: "running shoes", "laptops", "kitchen appliances"
  • Strategy: Show diverse products from the category
  • Ranking: High weight on category match, diversity across brands/attributes
{
  "intent_type": "category",
  "detection_patterns": [
    "category_name",
    "product_type"
  ],
  "ranking_strategy": {
    "category_match_weight": 0.7,
    "diversity_weight": 0.3,
    "apply_guardrails": true
  }
}

Attribute Intent​

Users searching by specific attributes:

  • Queries: "red dress", "wireless headphones", "waterproof watch"
  • Strategy: Prioritize products matching the attribute
  • Ranking: High weight on attribute match, filter by attribute
{
  "intent_type": "attribute",
  "detection_patterns": [
    "color + product_type",
    "material + product_type",
    "feature + product_type"
  ],
  "ranking_strategy": {
    "attribute_match_weight": 0.9,
    "relevance_weight": 0.1
  }
}

Use-Case Intent​

Users searching for a use case or problem:

  • Queries: "gift for mom", "workout gear", "home office setup"
  • Strategy: Show products that solve the use case
  • Ranking: Semantic relevance + use-case tags
{
  "intent_type": "use_case",
  "detection_patterns": [
    "for + use_case",
    "gift for",
    "best for"
  ],
  "ranking_strategy": {
    "semantic_relevance_weight": 0.6,
    "use_case_tags_weight": 0.4
  }
}

Comparison Intent​

Users comparing products:

  • Queries: "iPhone vs Samsung", "best running shoes"
  • Strategy: Show top products for comparison
  • Ranking: High-quality, comparable products
{
  "intent_type": "comparison",
  "detection_patterns": [
    "vs",
    "compare",
    "best"
  ],
  "ranking_strategy": {
    "quality_weight": 0.5,
    "popularity_weight": 0.3,
    "reviews_weight": 0.2
  }
}

Intent Detection​

Detect intent using multiple methods:

Pattern Matching​

{
  "detection_method": "pattern_matching",
  "patterns": {
    "brand": ["brand_name + product", "brand_name"],
    "category": ["product_type", "category_name"],
    "attribute": ["attribute + product", "color + product"]
  }
}

Machine Learning Classification​

{
  "detection_method": "ml_classification",
  "model": "intent_classifier_v1",
  "features": [
    "query_text",
    "query_length",
    "contains_brand",
    "contains_category",
    "contains_attribute"
  ]
}

Hybrid Approach​

Combine pattern matching and ML:

{
  "detection_method": "hybrid",
  "pattern_matching_weight": 0.4,
  "ml_classification_weight": 0.6
}

Intent-Specific Ranking​

Apply different ranking strategies per intent:

{
  "intent_strategies": {
    "brand": {
      "boost_exact_matches": true,
      "diversity": "low",
      "guardrails": false
    },
    "category": {
      "boost_exact_matches": false,
      "diversity": "high",
      "guardrails": true,
      "max_per_brand": 2
    },
    "attribute": {
      "filter_by_attribute": true,
      "boost_attribute_match": true,
      "diversity": "medium"
    }
  }
}

Ambiguous Intent Handling​

Handle queries with ambiguous or multiple intents:

{
  "ambiguous_intent_strategy": {
    "detection_threshold": 0.6,
    "fallback": "category",
    "show_diverse_results": true
  }
}

Intent Analytics​

Track intent distribution and performance:

  • Intent distribution: What percentage of queries are each intent type?
  • Conversion by intent: Which intents convert best?
  • Intent accuracy: How accurate is intent detection?
  • Intent-specific metrics: Performance metrics per intent type

Best Practices​

  1. Start with common intents: Focus on brand, category, and attribute intents first
  2. Validate detection: Regularly review intent detection accuracy
  3. Test strategies: A/B test intent-specific ranking strategies
  4. Handle ambiguity: Have fallback strategies for ambiguous queries
  5. Monitor performance: Track how intent handling affects conversion
  6. Iterate: Continuously improve intent detection and ranking strategies