#Assignment: PA 1 #####Written by: Noranda Brown #####Version: 2014.1.18

###CodeClimate

###Data The data set, ml-100k, consists of 100,000 ratings of 1682 movies from 943 users downloaded from http://www.grouplens.org. The main data set u.data consists of 100,000 rows where each row has 4 tab-separated items: user_id, movie_id, rating, and timestamp.

###Program Class: MovieData

initialize - Initializes instance variable @movies as a hash.

load_data - Reads in data from the original ml-100k/u.data file and stores movie objects in a hash.

popularity(movie_id) - Returns a number (0 - 100) that indicates the popularity (higher numbers are more popular). Note: Popularity is calculated by summing the ratings for the movie and normalizing the result. This takes into account not only the average rating, but also the number of times a movie has been reviewed. i.e. sum_ratings / num_ratings (to get the average rating) * num_ratings (to account for multiple reviews) = sum_ratings -> normalize

popularity_list - Generates a list of all movie_id’s ordered by decreasing popularity.

similarity(user1, user2) - Generates a number (0 - 100) which indicates the similarity in movie preference between user1 and user2 (higher numbers indicate greater similarity).

most_similar(u, number_of_users = 5) - Returns a list of the top number_of_users (default = 5) whose tastes are most similar to the tastes of user u.

other_users(u) - Private method that returns a list of all users except u.

Class: Movie

initialize(movie_id) - Initializes instance variables @movie_id, @user_ratings, and @sum_ratings.

add_rating(user_id, rating, timestamp) - Adds a user_id, rating and timestamp to a movie.

sum_ratings - Returns the sum of all ratings for a movie.

user_rated?(user_id) - Returns true if user_id has rated a movie and false otherwise.

user_rating(user_id) - Returns the user rating from user_id for a movie.

user_list - Returns a list of users that have reviewed a movie.

Class: UserRating

initialize(user_id, rating, timestamp) - Initializes instance variables @user_id, @rating, and @timestamp.

###Questions

1. Describe an algorithm to predict the ranking that a user U would give to a movie M assuming the user hasn’t already ranked the movie in the dataset.

predict_rating(user_id)
        sum = most_similar(user_id, 20).inject(0) { |sum, user| sum + user_rating(user) }
        count = most_similar(user_id, 20).inject(0) { |count, user| count + 1 if user_rated?(user) }
        (sum / count).to_i

This algorithm calculates the average rating of the top 20 most similar users.

2. Does your algorithms scale? What factors determine the execution time of your “most_similar” and “popularity_list” algorithms.

   While this algorithm would work, it would not scale very well, primarily due to the most_similar method which requires calculating the similarity between the user and all other users. Thus, the more users, the less efficient the algorithm.