Multiple prototypes already implement different definitions of "trending" (wme_pageviews batch moments comparison from enterprise, ), and multiple consumers want signals that mean subtly different things to each. This task produces a project document framing the problem, the algorithmic options, and an evaluation framework — giving PMs a shared decision aid for turning product requirements into engineering categories.
- Taxonomy of streaming-friendly trending definitions, with three worked examples from existing prototypes (batch moments comparison, streaming top-k, streaming state machine)
- Architectural implications (streaming vs batch vs hybrid) and operational trade-offs
- Evaluation framework: latency, quality, and operational-complexity criteria
- Offline evaluation harness (replay datasets + simple batch reimplementations of online algorithms) to make comparisons tractable in the absence of labelled data
Non-goals: picking a single production implementation; bot detection; complex offline algorithms (learned models, post-processing).