Formulation and usage questions

I have a few questions about using Spotlight for an item-item problem involving graded implicit feedback, pardon me if there is a better forum for such questions, I wasn’t able to find one.

I work on a system with feedback in the form of clicks (aka page view), likes and purchases. In this case obviously a purchase is substantially more desirable than a simple click.

Is there an obvious way to achieve this with Spotlight? Should I treat it as pure implicit and use the weights parameter to assign a greater weight to purchases than clicks? Or is it more appropriate to treat it as a ratings prediction problem where the “ratings” are really pseudo-ratings assigned by me?

Also, does Spotlight have any support for cold-start? Or support for predicting for a new user in production based on that user’s (previously unseen) history of implicit feedback? Or would lightfm maybe be a better fit for all of this?

Finally, if deployed in production can Spotlight models predict at reasonably low latency? Perhaps <100ms?

thanks very much for Spotlight. It’s well-documented and the code is a joy to read.

[arita37]

For negative sampling, what about GAN ? (IRGAN paper).

[maciejkula]

I wonder if the Turi implementation is to simulate a negative sampling scheme where sampling probability isn’t uniform. (As an aside, something like this would be a valuable addition to Spotlight.)

As the project stands at the moment, your best bet is to weigh observations by repeating them in the training set. This is equivalent to weights (but less computationally efficient).