This question is probably very repeated on blog sites and Q & A, but I have not yet found a specific answer.
I am trying to create a recommendation system for customers using only their purchase history.
- Say my application has n products.
- Calculate the similarities of elements for all n products based on their attributes (for example, country, type, price)
- When the user needs a recommendation, loop the previously purchased products p for user u and select similar products (the similarity is performed in the previous step).
If we are right, we call it a recommendation based on content, and not joint filtering, since it is not related to the coincidence of elements or user settings with the element.
My problem is multiple:
- Is there any existing scalable ML platform that solves competition-based recommendations (I'm fine to use different technologies / language).
- Is there a way to tune Mahout to get this result?
- Is classification a way to handle content-based recommendations?
- Is this something suitable for this task?
Note. I looked at Mahout (since I am familiar with Java and Mahout seems to use Hadoop for distributed processing) for the scale and benefits of having well-tested ML algorithms.
Your help is appreciated. Any examples would be really wonderful. Thank.
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