vrushabhshet Goto Github PK

-movie-recomendation-system-content-based

Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual. These results are based on their profile, search/browsing history, what other people with similar traits/demographics are watching, and how likely are you to watch those movies.Recommender systems produce a list of recommendations in any of the two ways CONTENT-BASED FILTERING : A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate. COLLABORATIVE FILTERING : To address some of the limitations of content-based filtering, collaborative filtering uses similarities between users and items simultaneously to provide recommendations. This allows for serendipitous recommendations; that is, collaborative filtering models can recommend an item to user A based on the interests of a similar user B. Furthermore, the embeddings can be learned automatically, without relying on hand-engineering of features. In this project we are recommending movies based on content filtering. I have used dataset of HOLLYWOOD FILMS.

applied-machine-learning-in-python

University of Michigan on Coursera

applied-machine-learning-in-python-1

workflow-management-system

Managing Employee Workflow