But you actually need vision because purchase history is not indicative of my future purchases. Sometimes I buy butter and eat it in a 3 days and buy again. Sometimes I’m not in the mood and have a chunk of butter to sit in my fridge for 3 weeks. It’s honestly totally random for a lot of things. It depends only on my mood at the moment.
You’d be surprised at how many of those things you think are random would actually emerge as a pattern in long enough purchase history data.
For example, it might be that there’s a seasonality to your being in the mood. Or other things you’d have brought a week before, etc.
Over a decade ago a model looking only at purchase history for Target was able to tell a teenage girl was pregnant before her family knew just by things like switching from scented candles to unscented.
There’s more modeled in that data than simply what’s on the receipt.
I agree, in the context of the tweet, that purchase history is enough to build a working product that roughly meets user requirements (at least in terms of predicting consumed items). This assumes you can find enough purchase history for a given user. Even then, I have doubts about how robust such a strategy is. The sparsity in your dataset for certain items means you will either a.) be forced to remove those items from your prediction service or b.) frustrate your users with heavy prediction bias. Some items also simply won’t work in this system - maybe the user only eats hotdogs in the summer. Maybe they only buy eggs with brownie mix. There will be many dependencies you are required to model to get a system like this working, and I don’t believe there is any single model powerful enough to do this by itself. Directly quantifying the user’s pantry via vision seems easy in comparison.
You don’t use vision, or if using it you are only supplementing a model that is mostly using purchase histories as the guiding factor.
But you actually need vision because purchase history is not indicative of my future purchases. Sometimes I buy butter and eat it in a 3 days and buy again. Sometimes I’m not in the mood and have a chunk of butter to sit in my fridge for 3 weeks. It’s honestly totally random for a lot of things. It depends only on my mood at the moment.
You’d be surprised at how many of those things you think are random would actually emerge as a pattern in long enough purchase history data.
For example, it might be that there’s a seasonality to your being in the mood. Or other things you’d have brought a week before, etc.
Over a decade ago a model looking only at purchase history for Target was able to tell a teenage girl was pregnant before her family knew just by things like switching from scented candles to unscented.
There’s more modeled in that data than simply what’s on the receipt.
I agree, in the context of the tweet, that purchase history is enough to build a working product that roughly meets user requirements (at least in terms of predicting consumed items). This assumes you can find enough purchase history for a given user. Even then, I have doubts about how robust such a strategy is. The sparsity in your dataset for certain items means you will either a.) be forced to remove those items from your prediction service or b.) frustrate your users with heavy prediction bias. Some items also simply won’t work in this system - maybe the user only eats hotdogs in the summer. Maybe they only buy eggs with brownie mix. There will be many dependencies you are required to model to get a system like this working, and I don’t believe there is any single model powerful enough to do this by itself. Directly quantifying the user’s pantry via vision seems easy in comparison.