Hi, I am building a product recommendation engine for an e-commerce website and I do not have explicit rating values given by users to products. I instead have implicit feedback from the views, add to carts and purchases and I generate a preference score using a linear combination of these.
I built a recommender using the parallel ALS-WR recommender for implicit data in mahout. However the results do not entirely make sense. This could be because around 50-70% users are infrequent users who have just viewed a couple of products throughout their history. I am still trying to tune the input parameters to the algorithm and clean up the data to get a denser data set. However, I am wondering if a item based collaborative filtering approach based on boolean preferences is a better option for my problem. I could use the 'TanimotoCoefficientSimilarity' or the 'LogLikelihoodSimilarity' for this. Are there other alternative approaches besides these that I can try ? I would love to hear some feedback. Thanks. Regards, Sneha
