Description
A guide to build a content-based movie recommender model based on NLP.
Summary
- When we provide ratings for products and services on the internet, all the preferences we express and data we share (explicitly or not), are used to generate recommendations by recommender systems.
- Exploring the dataset, there are 250 movies (rows) and 38 attributes (columns).
- I used the Rake function to extract the most relevant words from whole sentences in the ‘Plot’ column.
- Once I have the matrix containing the count for all words, I can apply the cosine_similarity function to compare similarities between movies.