Hi, I have a boolean/binary where a customer and product id are found when the customer actually bought the product and not found it the customer did not buy it. The dataset represented like this:
Customer ID Product ID Preference (1: customer bought the product) 1 1 1 2 1 1 3 2 1 4 1 1 5 2 1 6 2 1 7 1 1 8 1 1 9 2 1 I have tried different approaches like GenericBooleanPrefUserBasedRecommender with TanimotoCoefficient or LogLikelihood similarities, but I have also tried GenericUserBasedRecommender with the Uncentered Cosine Similarity and it gave me the highest precision and recall 100% and 60% respectively. I am not sure if it makes sense to use the Uncentered Cosine Similarity in this situation, or this is a wrong logic ? and what does the Uncentered Cosine Similairty do with such dataset. Any ideas would be really appreciated. Thank you, Shady
