Hello, We have come across situation where one similarity measure doesn't give desired result.
E.g we use PearsonCorrelationSimilarity to get similarity between users but find that only overlaps in ratings between the 2 users are being considered in final result, without consideration of the overall population. However, instead of resorting to a different measure, e.g. LogLikelihoodSimilarity or TanimotoCoefficientSimilarity, we are of opinion that we mix 2 or 3 measures so that weakness of one is booted by strength of the other. This is still experimental and don't how final outcome will be. But was just wondering if mixing similarity measures is advisable in the 1st place? Thanks. Mugoma Joseph.
