I'm looking for an algorithm to sort website results by popularity.. like Reddit's so the older a post the less power it's votes/score has.
Here is the generally accepted solution as used by reddit:
t = (time of entry post) - (Dec 8, 2005)
x = upvotes - downvotes
y = {1 if x > 0, 0 if x = 0, -1 if x < 0)
z = {1 if x < 1, otherwise x}
rank = log(z) + (y * t)/45000
I've been over Reddit's algorithm and although it will fit for one situation, what I really need is two algorithms, one for Popular posts and another for Upcoming posts:
Popular will decay slower, giving more weight to slightly older posts where Upcoming posts will focus more on popular posts today, dropping off sharply after N hours/days/etc.
I'm writing this using Sphinx expressions so I can't write a hugly complicated algo and I only have access to the following functions:
http://sphinxsearch.com/docs/current.html#numeric-functions
So I have the following data per post:
Here is my current solution:
Exponent = 0.01 (Popular), 0.5 (Upcoming)
SecondsSincePublised = abs(CurTimeInSecondsSinceDate-PubTimeInSecondsSinceDate)
Rank = (log10(PostScore)*10000) / pow(SecondsSincePublised,Exponent)
Although this solution does work its not ideal. A new and popular post in the last couple of hours often ranks high in both popular and upcoming which is not really what I want.
Can anyone suggest another algorithm that I can modify an exponent component to adjust the decay?
Have you tried ranking algorithm used by Hacker news? It is simple to implement.
Score = (P-1) / (T+2)^G
where,
P = points of an item (and -1 is to negate submitters vote)
T = time since submission (in hours)
G = Gravity, defaults to 1.8 in news.arc
You can vary Gravity to adjust the decay.
For more information refer to How Hacker News ranking algorithm works