Enhanced Name Clustering

At Namey, our goal is to help you find the perfect baby name. We've recently enhanced our recommendation engine to make even more personalized suggestions based on your preferences. Our improved algorithm now delivers more nuanced, context-aware name suggestions.

For example, if you tell us you like names like Annie and Sophie, our enhanced clustering system can now detect subtle patterns in your preferences. We've improved our underlying embedding model to capture not just sound and spelling similarities, but also cultural connections and historical trends. This means we can recommend names like Emma or Lily, that share important characteristics with your favorites.

For example, our previous model might have grouped names primarily by sound patterns. But our enhanced model can now recognize that "Siobhan" and "Niamh" share Irish heritage, even though they don't sound similar. Similarly, it can identify that "Luna" and "Aurora" both have celestial connections, despite different linguistic origins.

Our latest upgrade combines machine learning techniques with rich cultural data. We now use a hybrid approach that considers name list memberships alongside traditional embedding analysis. By analyzing how names are grouped in curated lists (like Irish Names or Shakespearean Names), we can offer more contextually relevant suggestions that align with your specific interests and heritage.

The visualization above represents our enhanced clustering model. We've applied dimensionality reduction techniques (t-SNE) to map names into a two-dimensional space where similar names appear closer together. The colors represent distinct clusters identified through K-means clustering. Our new approach creates more nuanced groupings and helps identify connections between names that traditional methods might miss.

Technical Implementation

Our enhanced clustering model uses a hybrid approach that combines vector embeddings with list membership data. We've reduced the dimensionality of our original embeddings and augmented them with categorical data about which curated lists each name belongs to.

This approach allows us to capture both semantic similarities between names and cultural/historical connections that might not be apparent from the names themselves.

We've also improved how we handle naming popularity - as you zoom into specific clusters, you'll see both popular favorites and unique treasures. Our enhanced algorithm now balances familiarity with uniqueness based on your preferences, helping you discover distinctive names without straying too far from your comfort zone.