Recommender systems
A recommender system is an information filtering system which is used in prediction of customer’s preference. Its main aim is the generation of meaningful recommendations to a certain number of clients for products or services that they might be interested in. For recommender systems to fulfil buyers and sellers need they undertake data analysis on the recommendations generated from the clients.
Recommender systems differ from data mining system in that data mining analyzes and distributes information from social activity records like newspapers whereby recommender systems use that information to make a recommendation to a client according to their preference.
Collaborative filtering methods are those methods whose basis is only on past interactions which were recorded by most users. Content-based filtering system its basis is on profile attributes. Lastly, hybrid techniques combine both of the two designs.
Amazon is e-commerce that deals with plenty of industries whereby it deals with payments, data storage, media, logistics and hardware. It deals with online shoppers whereby buyers order a product and is delivered at their destination according to their preference. Amazon mission is “ Earth’s biggest selection and to be Earth’s most customer-centric company “. An example of Amazon is Amazon Prime which is an annual membership program. Amazon Prime includes free shipping which is not limited and still is diversified to a media service which has access to instant streaming which is not limited therefore you can stream thousands of TV episodes and movies. Netflix is a streaming service that ensures that all members can watch documentaries, TV shows and internet-connected devices. Example of how you can watch a movie on Netflix is by purchasing a membership plan which can be standard definition, high definition and ultra-high definition. The standard definition is one screen plan SD, high definition is two-screen plan HD and ultra high definition is four-screen plan HD/UHD 4K.