Decode Real-World Examples in Python | Educative.io Course Notes

Building simple features and understand the underlying patterns that can be applied to real-world examples

• Create in 2022.02.14. Monday; Finish Feature 1 of Netflix

1. Netflix Features

Netflix is the biggest video streaming platform in the world, offering movies, seasons, biographies, reality shows, and more.

• Their video repository is growing significantly. So the engineering team at Netflix keeps trying to find better ways to display content to their consumers.
• Goal: improving the user experience in finding content to watch.
  • This involves the improvement of the search as well as recommendation functionality.

8 Features:

• Group Similar Titles: enable users to see relevant search results despite minor typos.
• Fetch Top Movies: Enable the user to view the top-rated movies worldwide, given that we have movie rankings available separately for different geographic regions.
• Find Median Age
• Popularity Analysis
• Fetch Most Recently Watched Titles
• Fetch Most Frequently Watchend Titles
• Browse Ratings
• Verify Session

1.1. Feature 1: Group Similar Titles with Typos

Question: How would we efficiently implement a functionality so that if a user misspells speed as spede, they are shown the correct title?

• We want to split the list of titles into sets of words so that all words in a set are anagrams.
  • ["duel", "dule", "speed", "spede", "deul", "cars"] ==> {"duel", "dule", "deul"}, {"speed", "spede"}, and {"cars"}

Solution：

For each title, compute a 26-element vector. Each element in this vector represents the frequency of an English letter in the corresponding title.

• This frequency count will be represented as a tuple.
• For example, abbccc will be represented as (1, 2, 3, 0, 0, ..., 0).
• This mapping will generate identical vectors for strings that are anagrams.

Use this vector as a key to insert the titles into a Hash Map.

• All anagrams will be mapped to the same entry in this Hash Map.
• When a user searches a word, compute the 26-element English letter frequency vector based on the word.
• Search in the Hash Map using this vector and return all the map entries.

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Complexity: Time: O(n x k), n is the size of the list of strings, k is the maximum length that a single string can have

Space: