There is a problem I've wanted to solve for a long time. Suppose you want to find antipodes in preferences: "axes of interest". In movie preferences, Star Trek movies (male nerds) v.s. Sex In The City (middle class women) might be one axis v.s. historical documentaries v.s. 1950's Douglas Sirk melodramas (don't ask). These axes are not orthogonal. (I saw this analysis in a presentation by one of the Netflix Competition finalists. Unfortunately, I did not ask him how to make it.)
Thank you for this hint. Negative correlations make this possible. Given an item-item matrix of Pearson distances, how would you isolate these axes? The minimum and maximum movies are easy to find. Each axis endpoint is a small cluster inside a genre. How would you find these small clusters? They're not orthogonal, so a naive SVD would not help. What is a good algorithm for this? Lance ----- Original Message ----- | From: "Paulo Villegas" <[email protected]> | To: [email protected] | Sent: Monday, November 26, 2012 2:03:59 PM | Subject: Re: Recommender's formula | | [...] | | They can be negative for certain similarity metrics, most notably | Pearson (which has sign, negative similarities express negative | correlations), other similarity metrics are strictly positive and | therefore do not present that problem. | | [...]
