data-algo-social

Build a cross-platform feed ranking pipeline by collecting signals, normalizing them with a unified taxonomy, and applying multi-stage candidate generation, graph-based discovery, scoring, and safety checks. This provides production-grade feed serving.

Graph-based discovery in social feed algorithms is a candidate generation mechanism that maps user relationships and interactions across networks. It enables in-network and out-of-network recall to surface relevant content beyond immediate follows.

Normalize social signals across multiple platforms by applying an inverted-index taxonomy that standardizes likes, comments, follows, views, dwell time, and negative signals into a uniform format for consistent ranking workflows.

Yes, this approach suits production-grade social feeds at scale by providing end-to-end pipeline patterns for signal collection, candidate generation, ranking, serving, and per-project algorithm profiling with a shared archive format.

Start designing a multi-stage ranking workflow by setting up a minimal project using the inverted-index signal taxonomy, then progressively add candidate generation, ranking, and safety hooks to expand the pipeline.

Safety checks in a social feed ranking pipeline are required to filter negative signals and govern content serving. They ensure cross-platform taxonomy mapping maintains safe and compliant feed outputs during production.