likeconnected

Likeconnected is a conceptual approach used in social networks and content platforms to describe the connections between users derived from their expressed likes. The central idea is that users who share a number of liked items are likely to have related interests, and therefore may be more likely to engage with each other or with recommendations derived from those interests.

Implementation often models the system as a bipartite graph with one set of nodes representing users and another representing items that can be liked. An edge connects a user to an item when the user has liked it. The user-to-user similarity can be computed by projecting the bipartite graph onto the user set, with edge weights proportional to the number of shared liked items, optionally normalized by similarity measures such as Jaccard similarity or cosine similarity.

Applications include friend or follower suggestions, content recommendation, community detection, and A/B testing of engagement strategies. Variants may incorporate recency weighting, item quality, and user activity levels to balance freshness and reliability.

Limitations and concerns include susceptibility to popularity bias, privacy considerations, cold start for new users, and potential manipulation through mass liking or sockpuppet accounts. Careful evaluation and governance are typically required when deploying likeconnected-based features.

Related concepts include collaborative filtering, content-based filtering, social graphs, and affinity scoring.