How Does a Recommendation Engine Work With Predicting Likes?

Recommendation Engine Work with Predicting Likes


  • How Does a Recommendation Engine Work With Predicting Likes?

    Sanskriti Singh

    Sanskriti is a tech writer and a freelance data scientist. She has rich experience into writing technical content and also finds interest in writing content related to mental health, productivity and self improvement.

Frequently Asked Questions

There are primarily four different phases in the working of a recommendation engine: data collection, data storage, data analysis, and data filtering. Under the data analysis phase, there is a recommender function which considers specific user information and further predicts the rating that the user might assign to a product. The predicted recommendation is then retrieved under the filtering phase.

A recommendation system is a part of predictive analytics that uses various machine learning, deep learning, and advanced algorithms to predict the recommendation against a specific user or context.

There are several algorithms, such as clustering-based algorithms, K-nearest neighbors, matrix factorization, etc., on which a recommendation engine is built upon.

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