问题描述：lintcode506
Give a user a list of movies he has seen and recommend other movies he may like for each user. Here we specify a simple recommendation algorithm that you need to implement in Map Reduce.
For a user, join the movies A, B, C that he has seen, then we can see which movie collections these people have seen among the people who have seen the movies A, B or C. For these movies, the first five movies with the highest frequency are recommended to the user. The recommended movies need to be sorted according to the degree of correlation
思路：
Map Reduce思想运用得不够好，解题逻辑是分三步：第一步，分别以每个电影为key，计算用户看此电影的次数（此题中看过记为1次，没看过的不标记，没有多次看同一个电影）。第二步，计算共现矩阵，即用户数为矩阵行数、列数，如果两个用户同事看过相同的电影，则共现，标为1，如果没有同时看过相同的电影，则标为0。例共现矩阵第一行所示用户，标为1的列元素下标，即为与第一行用户共现的用户。第三步，剔除当前用户，所有与当前用户共现用户看过的电影次数叠加，并按照次数排序，根据排序，推荐次数最多的5部电
