what is the sense to demo your code on data without auxiliary features, when you claim auxiliary feature specific code ?

[Sandy4321]

may you clarify how your code works with key advertised feature
as written in
https://towardsdatascience.com/factorization-machines-for-item-recommendation-with-implicit-feedback-data-5655a7c749db

To overcome these limitations we need a more general model framework that can extend the latent factor approach to incorporate arbitrary auxiliary features, and specialized loss functions that directly optimize item rank-order using implicit feedback data. Enter Factorization Machines and Learning-to-Rank.

but you testing your code on data without auxiliary feature
as you wrote
Unfortunately, there are no user auxiliary features to take advantage of with this data set.

what is the sense to demo your code on data without auxiliary features, when you claim auxiliary feature specific code ?