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From Sneha Venkatesh <>
Subject Recommendations for products on e-commerce website
Date Fri, 11 Jul 2014 15:51:14 GMT

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

Are there other alternative approaches besides these that I can try ? I
would love to hear some feedback. Thanks.


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