Feature #1: Group Similar Titles
Discover how to implement a feature that groups similar titles by treating anagrams as sets. Learn to compute character frequency vectors for each title, use them as keys in a hash map, and retrieve correct matches for misspelled user queries efficiently. This lesson enhances your understanding of hashing techniques and their application in search and recommendation systems.
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 you 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 that the frequency of each alphabet in words belonging to the same group is equal. In the set [["speed", "spede"]], the frequency of the characters s, p, e, and d are the same in each word.
Let’s see how we might implement this functionality:
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For each title, compute a ...