I'm trying to predict what a user might eat next give history of a user's food logs and a users demographic data.
The data that I have for each user is:
- Food logs (Where users track what foods they eat throughout the day)
- Location (City, Country)
Using this data, since food logs is a time series dataset the first thing that comes to mind is using something like an LSTM, but I'm not sure how the rest could factor in. Another option is creating buckets of users using rest of the data and then applying a time series model to the users in each bucket, but in this case each model might not have enough data to train to a good accuracy. Is there some model that can combine both types of data? If not, what would be your approach to solve this problem?