I have a boolean input dataset, with user, item, and preference. Each preference is a 1.0 if it exists. Based on this dataset I had used a Tanimoto Similarity and tried both Boolean Pref User and Item Recommenders.
After reading Mahout in Action and several threads on stack overflow, I saw that the LogLikelihood Similarity model was recommended for boolean dataset recommenders. However, the scores I get for the recommended items using the LogLikelihood similarity are sometimes much greater than 1.0, even though none of the input scores are higher than that. I saw scores of 11.0 being returned for some users' recommendations. This is making it very hard for me to use the scoring and estimation functions. I have switched back to Tanimoto for now, but am I doing something wrong, or am I incorrect in expecting the recommended scores and estimated preferences to be in the 0-1.0 range for this dataset?
