Recommender Systems with Machine Learning - Recommendation Engine

The video tutorial explains how to build a recommendation engine by creating functions to convert between movie titles and indexes. It introduces the Fuzzy Wuzzy library for matching similar titles and demonstrates how to develop functions to find the closest titles using matching scores. The tutorial concludes with implementing logic to sort and compare movie titles based on their similarity scores.

What are the two different functions needed for the recommendation engine?

Explain the purpose of the function 'get title from index'.

What is the role of the Fuzzy Wuzzy library in the recommendation engine?

Describe how the 'matching score' function works.

How does the function 'find_closest_title' utilize the previously defined functions?

What is the significance of sorting the scores in the recommendation process?

What will be the next step after creating all the functions for the recommendation engine?