From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect
The article traces the historical development of recommendation systems from early manual and hot‑ranking methods through natural ranking and machine‑learning‑based scoring, discusses the Matthew effect and its mitigation via randomization, multi‑objective weighting, and pipeline architectures, and outlines modern personalization and recall strategies for e‑commerce platforms.
