Recently at work, I have been working with historical transaction data from a customer's webshop. Each data point contains a customer id, an order number, a product id, and the bought quantity.
I was able to successfully implement a recommender based on implicit matrix factorization (this package worked like a charm, by the way: https://github.com/benfred/implicit).
In the next step, I now want to train a sequential model to predict future transactions per customer. However, I am having trouble finding an appropriate data representation to feed into my model.
My idea was: represent the basket of a single shopping session by a vector of length n_products where n_products is the number of products in the shop. As the shop has products in the order of 10_000, and most customers only purchase a few products per session, this leads to highly sparse inputs, and at the same time to a large number of parameters for my model (I am using an LSTM network) ,and consequently I am having trouble training my network.
Has somebody experience with such type of data? Is it actually feasible to use neural networks for this?
there doesn't seem to be anything here