Question

I’m curious how to do normalizing of numbers for a ranking algorithm

let’s say I want to rank a link based on importance and I have two columns to work with

so a table would look like

url | comments | views

now I want to rank comments higher than views so I would first think to do comments*3 or something to weight it, however if there is a large view number like 40,000 and only 4 comments then the comments weight gets dropped out.

So I’m thinking I have to normalize those scores down to a more equal playing field before I can weight them. Any ideas or pointers to how that’s usually done?

thanks

Answer 1

For each url, you could first normalize the comments and views to a percentile. For example,

 comment_percentile = (comments - min(comments)) / (max(comments) - min(comments))
 views_percentile = (views - min(views)) / (max(views) - min(views))

Then you could assign weights to each of the percentile values to compute the overall score.

 url_score = (comment_percentile_weight * comment_percentile) + (views_percentile_weight * views_percentile)

Additional strategies may involve eliminating outliers if the values cluster toward one end of the range.