Thanks Sean. I get your point. Will try incorporating that.
Earlier, as I mentioned, for a small item count(<5000), the input(datamodel) to
the recommender was nC2 item-item pairs(tried to feed uniform preference for
each item to every other item), without the rating field, and then called
recommender.mostSimilarItems() to get the list. nC2 works, but is not scalable.
It worked well as the recommendations were the similar items(that works for me
now).
Although am digging through the code to see what least input I can give, any
meaningful suggestion for data input would be awesome.
