Recommendation engines connect users to items whose embeddings are close in similarity space, then decide how much to also explore beyond that comfort zone.
score(u,i) = cosine(embed_u, embed_i)
recommend if score > threshold
- Users — user nodes on one side of the bipartite graph.
- Items — product/content nodes on the other side being recommended.
- Similarity threshold — collaborative-filtering similarity score needed before a user-item edge lights up.
- Diversity injection — how often the system deliberately suggests a lower-similarity item to avoid a filter bubble.
Every major streaming and shopping platform runs a version of this bipartite similarity graph at massive scale to decide what you see next.