Content Based: If you can represent your products as a vector, you can have a distance between each product, then you have a item-item recommendation. You can use all kinds of embedding to achieve this results, some techniques that we tried are word2vec embedding of user navigation, auto encoding of features using neural networks, dimensionality reduction with PCA, ALS, etc. There are lots of libs for solving these problems as is a very studied field, usually numpy and for finding the neighbors we use ann from scikitlearn, because if you have millions of items, you cant just find the distance between all the pairs.
Collaborative - Filtering, here you use the pairs of behavior of the users, <user, item, ranking>. There is a surprise lib in python that works well, you have the MlLib from Spark too, this techniques are called matrix factorization techniques, and also gives you a embedding of the item or the user, and you can apply the techniques of content based to find user-user and item-item recommendations along the user-item recommendations
Hybrid Models: These are the models that use behavior and features of the user an items, LightFM is a good lib that works well, but you can model it with other tools like neural networks ( https://ai.google/research/pubs/pub45530 ).
The challenges are depending on the company, its not the same to recommended small amount of items to large number of users than large number of items to small number users.
There is a whole specialization in coursera that is really good https://www.coursera.org/specializations/recommender-systems