A recommendation system’s effectiveness (for all the application domains) proves only if it is trained on multiple datasets and gives consistent results. This is where Alie offers a clear advantage by allowing you to test multiple datasets and algorithms for desired results.
Training data is a key input for Machine Learning (ML) systems that comprehend such data and uses the information for future predictions. The more data you provide to the ML system, the faster that model can learn and improve.
Alie allows you to add your own datasets, which helps in offering action-based recommendations to your end-users. Such recommendations are based on other users’ history, items purchase, items viewed, etc.
Providing recommendations in multiple domains requires support for training of multiple datasets, heterogeneous data structures, domain-specific algorithm customization. In this feature we talked about multiple datasets, Alie also supports other features, which a true domain agnostic recommendation system should provide.