How to Build a Product Recommendation System. Machine Learning Solutions

One pressing issue of product recommendation systems today is the scalability of algorithms with large, real-world datasets. It's possible that a recommendation algorithm will work well and produce accurate results with small datasets, yet may start producing inaccurate or inefficient results with large ones. In addition, some algorithms are computationally expensive to run – the larger the dataset, the longer it will take, and the more it will cost the business to analyse and make recommendations from it. Advanced, large-scale assessment methods are required to deal with both issues.