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.
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.