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How to rank a million images with a crowdsourced sort

I'd like to rank a collection of landscape images by making a game whereby site visitors can rate them, in order to find out which images people find the most appealing.

What would be a good method of doing that?

  • Hot-or-Not style? I.e. show a single image, ask the user to rank it from 1-10. As I see it, this allows me to average the scores, and I would just need to ensure that I get an even distribution of votes across all the images. Fairly simple to implement.
  • Pick A-or-B? I.e. show two images, ask user to pick the better one. This is appealing as there is no numerical ranking, it's just a comparison. But how would I implement it? My first thought was to do it as a quicksort, with the comparison operations being provided by humans, and once completed, simply repeat the sort ad-infinitum.

How would you do it?

If you need numbers, I'm talking about one million images, on a site with 20,000 daily visits. I'd imagine a small proportion might play the game, for the sake of argument, lets say I can generate 2,000 human sort operations a day! It's a non-profit website, and the terminally curious will find it through my profile :)

Solution

  • As others have said, ranking 1-10 does not work that well because people have different levels.

    The problem with the Pick A-or-B method is that its not guaranteed for the system to be transitive (A can beat B, but B beats C, and C beats A). Having nontransitive comparison operators breaks sorting algorithms. With quicksort, against this example, the letters not chosen as the pivot will be incorrectly ranked against each other.

    At any given time, you want an absolute ranking of all the pictures (even if some/all of them are tied). You also want your ranking not to change unless someone votes.

    I would use the Pick A-or-B (or tie) method, but determine ranking similar to the Elo ratings system which is used for rankings in 2 player games (originally chess):

    The Elo player-rating system compares players’ match records against their opponents’ match records and determines the probability of the player winning the matchup. This probability factor determines how many points a players’ rating goes up or down based on the results of each match. When a player defeats an opponent with a higher rating, the player’s rating goes up more than if he or she defeated a player with a lower rating (since players should defeat opponents who have lower ratings).

    The Elo System:

    1. All new players start out with a base rating of 1600
    2. WinProbability = 1/(10^(( Opponent’s Current Rating–Player’s Current Rating)/400) + 1)
    3. ScoringPt = 1 point if they win the match, 0 if they lose, and 0.5 for a draw.
    4. Player’s New Rating = Player’s Old Rating + (K-Value * (ScoringPt–Player’s Win Probability))

    Replace "players" with pictures and you have a simple way of adjusting both pictures' rating based on a formula. You can then perform a ranking using those numeric scores. (K-Value here is the "Level" of the tournament. It's 8-16 for small local tournaments and 24-32 for larger invitationals/regionals. You can just use a constant like 20).

    With this method, you only need to keep one number for each picture which is a lot less memory intensive than keeping the individual ranks of each picture to each other picture.

    EDIT: Added a little more meat based on comments.