hello, i will appreciate if you help me to solve my problem.
i want to use LSTM for below data structure. i want to predict next rating of review.
first: business rating (1-5 stars)
Business_1 = [1, 3, 4, 5, 2, 4, 1, 1, 4, 5, 2, 1]
Business_2 = [3, 3, 2, 2, 3, 4, 5, 2, 4, 1, 5, 4]
Business_3 = [3, 2, 1, 2, 1, 4, 5, 2, 1, 1, 2, 4]
.
.
.
Business_N = [3, 2, 1, 1, 1, 5, 3, 3, 3, 2, 1, 1]
my project is not univariate and it’s multivariate. so besides of rating we have other numerical features.
I want to predict next rating based on sequence length: L.
I want a model to use all business rating at the same time. I don’t want to use one by one ratings.
Business_1 = [1, 3, 4, 5, 2, 4, 1, 1, 4, 5, 2, 1]
convert to L=7
data, label
[1, 3, 4, 5, 2, 4, 1], [1]
[3, 4, 5, 2, 4, 1, 1], [4]
[4, 5, 2, 4, 1, 1, 4], [5]
[5, 2, 4, 1, 1, 4, 5], [2]
[2, 4, 1, 1, 4, 5, 2], [1]
the top data structure is 57Features
now i consider all business. so the data structure will be:
N: total number of business
i think the shape of data structure like: N* 57Features
My question is:
How should I use this data structure in LSTM? i want model consider all business at the same time and predict future business rating based on business.
thanks for helping me in advance.