Ranking algorithm for Bear Blog discover page

• May 25, 2025 at 21:20 IST
• 1 min read

• tech
• coding

On the Bear Blog discover page, they have the below-mentioned snippet that explains how articles are ranked:

This page is ranked according to the following algorithm:

Score = log10(U) + (S / B * 86,400)

Where,

U = Upvotes of a post

S = Seconds since Jan 1st, 2020

B = Buoyancy modifier (currently at 14)

--

B values is used to modify the effect of time on the score. A lower B causes posts to sink faster with time.

I asked ChatGPT to explain how this works and ranks articles, and I learned the below:

• The log10(U) part means that as a post gets more upvotes, its score increases, but each additional upvote has a slightly smaller effect than the previous one. This helps prevent very popular posts from dominating the rankings indefinitely.
• The (S / (B * 86,400)) part adds a time-based component to the score. Since there are 86,400 seconds in a day, this part increases the score as time passes, giving newer posts a chance to appear higher in the rankings.
• The buoyancy modifier B controls how quickly the time component affects the score. A lower B value would make posts "sink" faster over time, while a higher B value would allow them to stay prominent longer.

I think, it's very interesting and, someday, it would really be useful for something that I build in the future.