Movies recommendation system using collaborative filtering and k-means
Phongsavanh Phorasim, Lasheng Yu · International Journal of Advanced Computer Research · 2017
IntroductionCurrently as a new technic of advertisement, we use sets of information such as movies, music and other form of technique to convince customers.Technology is developing so fast and dissemination of knowledge has increased, as well as the needs of consumers that are more complex.Manufacturers and suppliers had difficulty in offering products and services that meet customer needs for the convenience of buying the service because of business which makes the competition even more active.In this era of competition, complex information causes overload problems which in turn are time consuming.Recommendation systems are information filtering system that aids users in predicting rating or preference of an item under users' consideration.The systems offer users alternate selections without having to work out all the details by themselves.As overwhelming information explosion renders searching, extraction, analysis, and processing hideous and formidably time-consuming operations, recommender systems became a favorable decision tool or assistant to offload such undesirable tasks.Worse yet, activities involving human are inevitably subject to human errors that can lead to poor or wrong decisions.