Practice/Atlassian/Leetcode 1341. Movie Rating

Leetcode 1341. Movie Rating

CodingOptional

Problem

You are analyzing a film review database containing information about users, movies, and the ratings users have given to movies. Your task is to identify two key pieces of information:

1. Most Active Reviewer: Find the user who has rated the greatest number of distinct movies across all time. If multiple users are tied for the most reviews, select the one whose name comes first alphabetically.
2. Top Rated Movie in February 2020: Find the movie with the highest average rating based only on ratings submitted during February 2020. If multiple movies are tied for the highest average, select the one whose title comes first alphabetically.

You are given three data structures:

• users: A list of [user_id, user_name] pairs
• movies: A list of [movie_id, movie_title] pairs
• ratings: A list of [user_id, movie_id, rating, created_date] entries where rating is an integer from 1-5 and created_date is in "YYYY-MM-DD" format

Return a string with two lines: the user's name on the first line and the movie's title on the second line.

Requirements

1. Count distinct movies rated per user across all time periods
2. Break ties for most active reviewer by selecting the lexicographically smallest user name
3. Calculate average ratings for movies using only ratings from February 2020
4. Break ties for highest average rating by selecting the lexicographically smallest movie title
5. Return results as a two-line string: user name followed by movie title

Constraints

• 1 ≤ number of users ≤ 1000
• 1 ≤ number of movies ≤ 1000
• 1 ≤ number of ratings ≤ 10000
• All user IDs and movie IDs are unique
• Rating values are integers between 1 and 5 inclusive
• Dates are valid strings in "YYYY-MM-DD" format
• At least one rating will exist in February 2020
• User names and movie titles contain only alphanumeric characters and spaces

Examples

Example 1:

Input: users = [[1, "Alice"], [2, "Bob"], [3, "Carol"]] movies = [[101, "Inception"], [102, "Interstellar"], [103, "Dunkirk"]] ratings = [ [1, 101, 5, "2020-02-15"], [1, 102, 4, "2020-02-16"], [2, 101, 3, "2020-02-10"], [3, 103, 5, "2020-02-20"] ] Output: "Alice\nDunkirk" Explanation: Alice rated 2 distinct movies (Inception and Interstellar), which is more than Bob (1) and Carol (1). For February 2020 ratings: Inception avg=4.0, Interstellar avg=4.0, Dunkirk avg=5.0. Dunkirk has the highest average.

Example 2:

Input: users = [[1, "Zara"], [2, "Aaron"]] movies = [[101, "Zodiac"], [102, "Avatar"]] ratings = [ [1, 101, 5, "2020-02-10"], [2, 102, 5, "2020-02-11"], [1, 102, 3, "2020-01-05"], [2, 101, 4, "2020-02-12"] ] Output: "Aaron\nAvatar" Explanation: Both users rated 2 distinct movies. Aaron comes before Zara alphabetically. For February 2020: Zodiac avg=(5+4)/2=4.5, Avatar avg=5.0. Avatar wins, but if they were tied, Avatar would come before Zodiac alphabetically.