Building a recommender system using python-recsys (SVD) with implicit feedback rather than ratings? I am building a simple recommender system using recsys libraries.
http://ocelma.net/software/python-recsys/build/html/quickstart.html
Rather than "ratings data" I simply have implicit feedback of sales (binary 0 or 1) for each items for each user. 
Is it as simple as interpreting my rating as "1" for items where a sale has occurred and using SVD as is? 
Or will that not work at all?
(Im a relative beginner here).
 A: It's not entirely clear what algorithm does python-recsys implement, and how appropriate it is for the task at hand. It does provide metrics for rank-based evaluation, which suggests that it is at least somewhat applicable to the implicit feedback setting.
However, is is worth noting that the last commit in the python-recsys repository was added in November 2014. In light of this, I would suggest some alternative implementations with are (1) more actively maintained and (2) more suited for the implicit feedback problem:


*

*Implicit (benfred/implicit on Github): a fast Python implementation of a classic weighted alternative least squares algorithm for implicit feedback.

*LightFM (lyst/lightfm on Github): a fast Python implementation of a number of learning-to-rank algorithms for implicit feedback.

*Spotlight (maciejkula/spotlight): a neural-network toolkit for both implicit and explicit recommender models.


(Disclosure, I am the author of both LightFM and Spotlight.)
You can find a more exhaustive list of recommender libraries at the RecSys Wiki.
A: This will work but it's a very simple model which does not mean that is useless.
I've build a system where the implicit feedback is a weighted sum over 3 types of events: product view, product added to cart and purchase. For instance:
rating = number of views * 1 + number of times added to cart * 2 + number of purchases * 10

In that way I can have much less sparse datasets because we don't just count sales/purchases but other types of events that associates users with products.
