## Pearson Correlation Score

This post will cover another topic from Programming Collective Intelligence that is used to define similarities between items called Pearson correlation score, the formula for this algorithm looks like the following,

\begin{equation} r = \frac{\sum XY - \frac{\sum X \times \sum Y }{N}} {\sqrt{(\sum X^2- \frac{(\sum X)^2}{N}) - (\sum Y^2- \frac{(\sum Y)^2}{N})}} \end{equation}This calculation returns a value between -1 and 1. Two users with a similarity of 1 have rated every item identically. Unlike Euclidean Distance Score this formula doesn't need to be normalized. Pearson correlation score, also accounts for average ratings for each user, a user that rates everything 5 and a user that rates everything 1 will have a similarity of 1. This may or may not be the behavior you want depending on your situation.

(defn pearson [x y] (let [shrd (filter x (keys y))] (if (= 0 (count shrd)) 0 (let [sum1 (reduce (fn[s mv] (+ s (x mv))) 0 shrd) sum2 (reduce (fn[s mv] (+ s (y mv))) 0 shrd) sum1sq (reduce (fn[s mv] (+ s (Math/pow (x mv) 2))) 0 shrd) sum2sq (reduce (fn[s mv] (+ s (Math/pow (y mv) 2))) 0 shrd) psum (reduce (fn[s mv] (+ s (* (x mv) (y mv)))) 0 shrd) num (- psum (/ (* sum1 sum2) (count shrd))) den (Math/sqrt (* (- sum1sq (/ (Math/pow sum1 2) (count shrd))) (- sum2sq (/ (Math/pow sum2 2) (count shrd)))))] (if (= den 0) 0 (double (/ num den))) ))))

Using the same critics map from Euclidean Distance Score,

user=> (pearson (critics "Lisa Rose") (critics "Gene Seymour")) 0.39605901719066977 user=> (pearson (critics "Lisa Rose") {}) 0