Blog Rankings

Designing a Blog Ranking System

Every year a slew of top blog lists are published for every topic under the sun (think “Top 10 Blogs in Data Science for 2020”). Most are manually curated, many are biased by the business interests of the publisher, few are data-driven, and none give you a voice. This article is my plan for solving these problems by delivering an unbiased, data-driven, and crowd-sourced blog ranking system for the data science community and beyond.

System Overview

Here’s how the system will work.

  1. Anyone submits a blog for consideration.
  2. Editorial team reviews submissions for quality.
  3. Site metrics are collected via API data pulls for metrics like domain authority and Alexa rank.
  4. Users vote for their favorite blogs on the rankings page.
  5. Ranking algorithm considers votes, site metrics, and editorial factors.

It’s pretty simple in theory. But how do we ensure enough votes are collected? And why is it useful for Skillenai to build and maintain this ranking system?

Count All the Votes

Collecting enough votes should be easy. This system will be self-reinforcing. Here’s how.

  1. A blog owner submits their blog for consideration.
    • Get new readers.
    • Build brand / personal reputation.
    • Easy backlink to their site.
  2. Blog owner encourages fans to upvote their blog.
    • Want to rank higher and get more traffic.
    • Blogger shares on social media.
    • Blogger links to rankings page.
  3. Other bloggers discover rankings page.
    • Thanks to promotion efforts of previous bloggers.
    • Cycle starts over.

And if that’s not enough, votes will also come from users of Skillenai’s products. Which brings me to the answer to the other major question, why it’s useful for Skillenai to maintain these rankings.

What’s In It For Skillenai

Skillenai’s core product, the Career Wizard, recommends learning resources based on skills and goals. Users of this product can upvote (and have other types of engagement with) various resources, including entire blogs. Each of those votes will be tied to their skills profile. This data enables powerful personalization of recommended learning resources.

These votes also make the blog rankings much more interesting. Rankings can be filtered on particular skills to help users discover the most relevant resources for them.