Recommendation Engine Nasih

Nowadays, competition between companies became extremely high, as most of them release their products and services with the highest quality and with more features than you can imagine, and as consumers now have to choose from many choices available to them, Wakeb designed this recommendation system to facilitate this process as the system's algorithms filters the of different customers information based on their social media sentiment analysis to recommend them with the products that meets their interest most, in order to facilitate their buying process and save their time and effort searching for services and products Recommendation can be accomplished based on We can divide the users into multiple segments based on their preferences (user features) and then recommend items to them based on their segment.

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Nowadays, competition between companies became extremely high, as most of them release their products and services with the highest quality and with more features than you can imagine, and as consumers now have to choose from many choices available to them, Wakeb designed this recommendation system to facilitate this process as the system's algorithms filters the of different customers information based on their social media sentiment analysis to recommend them with the products that meets their interest most, in order to facilitate their buying process and save their time and effort searching for services and products Recommendation can be accomplished based on

We can divide the users into multiple segments based on their preferences (user features) and then recommend items to them based on their segment. We can recommend items to users according to its popularity among the other users. Upselling cross selling

features

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Usage of Recommendation System

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Media: Recommending News, Videos and Music.

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E-Commerce: Recommendation of products

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Jobs: Recommending job vacancies along with emailing newsletters with job offers.

How it works

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