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.
Enhanced End-user Experience
Elevate User Experience with Personalized Recommendations
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.
A little time invested in tuning your machine learning(ML) model could drastically improve customer experience, increase your service consumption, and reduce average sales cycle metrics. Multiple Dataset feature allows you to train your model on multiple datasets which helps fine-tune your model to offer accurate recommendations and improve the end-user experience over time.
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Recommendation Service for your Website or App | Create Personalized Experiences
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