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University of Business and Technology in Kosovo Knowledge Center Theses and DissertationsStudent Work72018THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS THE TROY CASE Done Mulan University for
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What Is a Recommender System? A recommendation system is a subset of machine learning that uses data to help users find products and content. Websites and streaming services use recommender systems to generate “for you” or “you might also like” pages and content.
Recommender systems are used in machine learning to predict the ratings or preferences of items for a given user. They are commonly used in e-commerce applications to suggest items that a user may be interested in.
Benefits of a Recommendation Engine Personalized Recommendations. Have you ever wondered why people turn to relatives and friends for advice rather than turning to someone new? Product Discovery. Provide Reports. Customer Satisfaction. Increase in Revenue.
A recommendation system uses the process of information filtering to predict the products a user will like and ingly rate the products based on users' preferences. A recommender system easily highlights the most relevant products to the users and ensures faster conversion.
Recommendation systems allow you to drive much higher conversions and enhance average order value. You can bring multiple data sets (historical data, real-time visitor behavior, and third-party insights) into a recommendation algorithm using a recommendations engine.
Recommender systems are information filtering systems that deal with the problem of information overload [1] by filtering vital information fragment out of large amount of dynamically generated information ing to user's preferences, interest, or observed behavior about item [2].

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