A MOVIE RECOMENDER SYSTEM BY COMBING BOTH CONTENT BASED AND COLLABORATIVE FILTERING ALGORITHMS

The computerized world we are living in has a ton of information and data that is utilized by an assortment of clients, for example, videos, books and articles. Different users like different content. Getting what each user likes can be irritating. Each online services provider always aims in having many clients. Recommender systems importance's arises in such situations. The recommender framework proposes to a client some substance for example e.g. movies and books depending on what the user likes. In this research, a new movie recommender system is proposed, that will be able to improve the existing recommender systems. With this new recommender framework, the client will receive an improved forecast contrasted with different frameworks that as of now exist for example content-based filtering and collaborative-filtering. In order to overcome the disadvantages of the both methods (collaborative filtering algorithm (unsupervised learning) and content-based filtering algorithm (supervised learning)), the new system combines both methods. This will bring up a more stable system compared to the existing ones.