Feature #1: Group Similar Titles
Explore how to group similar titles by leveraging character frequency arrays to identify anagrams. Understand how this method helps implement efficient search functionality, improving your problem-solving skills for coding interviews with real-world examples.
We'll cover the following...
Description
First, we need to figure out a way to individually group all the character combinations of each title. Suppose the content library contains the following titles: "duel", "dule", "speed", "spede", "deul", "cars". 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. In the above list, there are three sets: {"duel", "dule", "deul"}, {"speed", "spede"}, and {"cars"}. Search results should comprise all members of the set that the search string is found in. We should pre-compute these sets instead of forming them when the user searches a title.
Here is an illustration of this process:
Solution
From the above description, we see that all members of each set are characterized by the same frequency of each alphabet. This means ...