By contrast, the current ranking system leads to the popular becoming more popular — once you’re on the top charts, you have increased visibility, which leads to more reviews, which further cements your chart position (as long as you stay inside your semantic rating bucket).
Those of us who want to discover hidden gems really need the search functionality to work with us, not against us. We want a system where the top charts are self-correcting, rather than self-reinforcing. Otherwise we get a situation like Apple’s with frozen charts, shady tactics, and skyrocketing user acquisition costs.
PROBLEM: You are a web programmer. You have users. Your users rate stuff on your site. You want to put the highest-rated stuff at the top and lowest-rated at the bottom. You need some sort of “score” to sort by.
CORRECT SOLUTION: Score = Lower bound of Wilson score confidence interval for a Bernoulli parameter
Say what: We need to balance the proportion of positive ratings with the uncertainty of a small number of observations. Fortunately, the math for this was worked out in 1927 by Edwin B. Wilson. What we want to ask is: Given the ratings I have, there is a 95% chance that the “real” fraction of positive ratings is at least what? Wilson gives the answer. Considering only positive and negative ratings (i.e. not a 5-star scale), the lower bound on the proportion of positive ratings is given by: