User Similarity

User similarity quantifies the resemblance between users to enable collaborative personalization. When users are similar, the preferences and behaviors of one can predict those of the other. Similarity can be computed using behavioral overlap metrics like cosine similarity on interaction vectors, demographic proximity, embedding distance in learned latent spaces, or graph-based measures in social networks.

For growth teams, user similarity is the computational foundation for collaborative filtering, lookalike audience building, and user segmentation. AI has advanced similarity computation from simple interaction-based measures to deep learning models that capture nuanced behavioral patterns and latent preference dimensions. Growth engineers should choose similarity measures based on the available data and the intended application. For recommendation, behavioral similarity captures demonstrated preferences most directly. For audience expansion, a combination of behavioral and attribute-based similarity produces more robust lookalikes. Key implementation considerations include scalability, since computing all-pairs similarity across millions of users requires efficient algorithms like locality-sensitive hashing or approximate nearest neighbor search, and freshness, since similarity should reflect recent behavior rather than outdated patterns. Teams should validate similarity measures by testing whether users identified as similar actually respond similarly to recommendations and campaigns.