Movie Recommendation System Using Semi-Supervised Learning

In the current digital era, with the huge amount of technologies and tremendous amount of information being available at disposal over the Internet, a huge amount of data is made available to users. This results in a condition known as "information overload". Due to these, it is difficult for a person to search and access information for taking decisions to arrive at an effective conclusion. To perorate this nut, there are filtering systems for information, known as the recommendation system or recommendation engine, considered here in paper, that help a person in identifying significant and possible services or products of interest based on the preferences given by him/her. This results in searching through lots of results to find the one that the user actually needs. This can be in cases like searching for books, music, videos, job postings, etc. A recommendation system is hence needed to help recommend items to users which are more relevant and accurate and fulfils the user's needs and requirements. A movie recommendation system is used to recommend movies which match the user taste's and preferences. Several approaches exist to implement this system - popularity-based recommendation system, simple recommendation system, content-based filtering, collaborative-based filtering, metadata-based filtering, demographic-based filtering approach. In this paper we use the following approaches - simple recommendation system, content-based filtering approach, collaborative-based filtering approach.