Improve recommendations with multiple datasets
Alie’s “Multiple dataset” feature lets you add multiple datasets to train Alie for faster & improved recommendations. You can add multiple datasets from your local computer, using API/Webhooks integration or copy-pasting a Javascript code to your website.
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.
Recommendation Service for your Website or App | Create Personalized Experiences
Already using a platform? Alie team will help with Data Migration, Customizations, and Integrations. Switch to Alie today!
You can request a free live demo of Muvi Products with our platform experts. Our platform experts will understand your use case and provide a detailed walkthrough of our product.
Muvi will help with Data Migration, Customizations, and Integrations. Switch to Muvi today!
Migrate to MuviReach out to Muvi at: