Java Basic Item Based Recommendation System

You will be implementing a basic item-based recommendation system. Recommendation systems are widely used on the web for recommending products and services to users based on their past actions and interaction with the system. The input to your recommender system is a dataset consisting of 100,000 ratings from about 1000 users on about 1700 movies. I have provided sample .dat files for the movies and the ratings. First the [login to view URL]: Each line in the [login to view URL] represents a unique movie and has the following format: movie id | movie title | release date | video release date |etc. You only need to parse the first two attributes, movie_id, and movie_name. You can extract these attributes using the String. You can use the split method in the String class in java standard library to extract the fields that are separated by “\\|” delimiter. Second [login to view URL]: All ratings are contained in the file ratings.dat. Each line of [login to view URL] has the following format ( the fields are separated by tab): UserID MovieID Rating Timestamp Where: -UserIDs: the id of the user who gave rating - MovieIDs: the id of the movie for which the user gave rating - Ratings: a number in the scale (1-5) given to movie_ID by user_ID. For example, the following record means user id 1 gave rating 5 to the item id 1193 ( the last entry is the time stamp which is not used in this project) 1 1193 5 978300760 Your recommender system will predict the ratings that the user will give to the movies he/she has not rated yet. The output of your recommender system should be a file that contains the top 5 movie recommendations for every user in a descending order of their predicted ratings (i.e., the first movie recommendation should have the highest predicted rating). The format of the output file should be as follows: UserID MovieTitle1::predicted rating | MovieTitle2::predicted rating | MovieTitle3::predicted rating | MovieTitle4::predicted rating | MovieTitle5::predicted rating For example, user ID: 1 top 5 recommendations: Cyclo (1995)::4.382758930148253| Office Killer (1997)::4.24082725472752| Little City (1998)::4.234925942215377| Death in Brunswick (1991)::4.224473123463171| Mamma Roma (1962)::4.178130372636395| This means that the top 5 movie recommendations for user ID 1 are: cyclo (1995) with predicted rating 4.38, office killer (1997) with predicted rating 4.2, etc. What you need to submit: 1- All your java files. Please make sure to include comments in your code to make it understandable 2- A readme file including a general description of your files. 3- A document explaining the following: a. data structures that you used to store movie information and ratings and the algorithm that you used to compute the top 5 recommendation. b. Analysis of the order of magnitude efficiency (big –Oh) of your algorithm in terms of the number of users, and movies. Briefly explain how you derived the big-Oh.