Recommender System for Movies using Content-based Filtering

Abstract

Information filtering systems, which recommend appropriate information to users from enormous amount of information, are becoming popular. Recommender systems are programs which attempt to predict items that a user may be interest in and depends on information provided by the users to gather knowledge. Content –based filtering systems are based on profile attributes. In content-based filtering, items are matched either to a user’s interest profile or query on the basis of content. Content-based system does not use any preference data and provides recommendation directly based on similarity of items.Contentbased recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. This system is an implementation of recommender system for movies using content-based filtering method.Rated movies from IMDb are used in this system. And the users must register and fill the profile to get recommendation because it calculates recommendation score depend on the user profile. This system recommends movies with recommendation score to the user finally and is implemented by using Microsoft SQL server and C# programming.