Building Interest-Based Matching with AI Taxonomies
Beyond Simple Keyword Matching
When a dating app asks users to pick their interests, it is building a profile vector that powers compatibility scoring. Most apps use a flat list: "Hiking," "Photography," "Cooking," "Travel." Two users match if they share enough overlapping tags. But this approach misses the forest for the trees.
Consider two users: one tags "Oil Painting" and the other tags "Watercolor." A flat comparison sees zero overlap. A taxonomy-aware system recognizes both as children of "Painting" under "Visual Arts" under "Creative Pursuits" — and scores them as highly compatible.
Hierarchical Similarity Scoring
With a taxonomy, you can compute similarity based on the distance between nodes in the tree. Two interests that share a direct parent are very similar. Two interests that only converge at the root are weakly related. This gives you a continuous similarity score instead of a binary match/no-match.
The Wu-Palmer similarity metric is commonly used for this. It measures how deep the lowest common ancestor of two nodes sits in the tree relative to the nodes themselves. Deeper shared ancestors mean stronger similarity. This approach has been validated in computational linguistics for decades and translates directly to interest matching.
Designing the Right Interest Taxonomy
The structure of your taxonomy directly affects match quality. Too shallow and everything is broadly similar. Too deep and the tree becomes unwieldy for users to navigate. The sweet spot is typically 3-4 levels deep with 5-8 children per node.
Top-level categories should represent fundamentally different domains of human interest: Sports and Fitness, Arts and Culture, Technology, Food and Drink, Travel and Outdoors, and so on. Second-level nodes break these into recognizable subcategories. Third-level nodes are the specific interests users actually select.
Balance matters too. If "Sports" has fifty leaf nodes but "Music" has three, your matching algorithm will over-index on sports granularity. The AI agent in Tag Taxonomy helps here by analyzing your category structure and suggesting where to add depth or consolidate nodes to maintain balance across the tree.
Implementation Patterns
Once you have the taxonomy, store it in a way that supports efficient ancestor queries. The adjacency list model (each node stores its parent ID) is simplest. For read-heavy workloads, materialized path or nested set models let you query entire subtrees without recursive joins. Most modern databases also support recursive CTEs, making adjacency lists practical even for complex queries.
At query time, expand each user's selected interests to include ancestor nodes with decaying weights. A user who selected "Tennis" implicitly has interest in "Racket Sports" (weight 0.7) and "Sports" (weight 0.4). Compare these weighted vectors between users for a nuanced compatibility score that flat tags could never produce.