Recommender-System

Recommendation System produces a ranked list on which a user might be interested, in the context of his current and past choices.

• Subclass of Information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to them.
• Helps deciding in what to wear, what to buy, what stocks to purchase etc.
• Applied in variety of applications like Movies, Books, Research Articles.

Types of recommender systems

1. Content Based: The Recommendation system recommends other movies which are similar to the selected movie.

    F(movie) -> {movies}
   

2. Collaborative: The Recommendation system recommends movies which are rated highly by the similar users.

    F(movies, user) -> {movies}
   

Description

This project provides a user-based collaborative filtering algorithm for the recommendation of movies, which implements Pearson correlation and a machine learning algorithm.

One of the most crucial issues, nowadays, is to provide personalized services to each individual based on their preferences. To achieve this goal, recommender system could be utilized as a tool to help the users in decision-making process offering different items.

IDE Used

Eclipse Workspace

Language Used

Java

Mentor

Mr. Anupam Singh