Movie Recommendation System in Machine Learning

HeartBeat- Fritz AI

Today, let’s discuss something about the recommendations of the movies. A movie recommendation is an important aspect in our social life due to its strength in providing enhanced entertainment. Hence, A recommendation system can suggest a set of movies to users based on the popularities of the movies and their preference.

It is used for the purpose of suggesting items to purchase or to see. Hence, it is an important aspect in Artificial Intelligence. They direct users towards those items which can meet their needs through cutting down a large database of Information into smaller chunks. Hence, this system is generally a filtering system that seeks to predict the “rating” or “preference” a user would give to an item. It is generally used in commercial applications. 

Hence, collaborative filtering model is then in use to predict items (or ratings for items) that the user may have an interest in. 

Movie Recommendation System Process

Movie Recommendation System in Machine Learning-METHODS 

In the field of machine learning, classification methods which use different strategies to organize and classify data. Classifiers could possibly require training data. 

  • Collaborative filtering 
  • Content-based filtering 
  • Multi-criteria recommender systems 
  • Risk-aware recommender systems 
  • Mobile recommender systems 
  • Hybrid recommender systems

Some hybridization techniques include: 

  • Weighted: Combining numerically the score of different recommendation components. 
  • Switching: Among recommendation components, choosing and applying the selected one. 
  • Mixed: Recommendations are together to give the recommendation from different recommenders. 
  • Feature Combination: Features derived from different kinds of sources are combining together. 
  • Feature Augmentation: Computing a feature or set of features, which is the part of the input to the next technique? 
  • Cascade: Recommenders give high priority, compared with the lower priority ones.  
  • Meta-level

The 4 Recommendation Engines That Can Predict our Movie Preference-

Here’s a distribution of the user ratings:

user ratings

MovieLens Ratings

Here’s another word-cloud of the movie genres:

movie genres

MovieLens Genres

Hence, the top 5 genres are, in that respect order: Drama, Comedy, Action, Thriller, and Romance.

Now let’s move on to explore the 4 recommendation systems that can be used. Here they are, in respective order of presentation:

  1. Content-Based Filtering
  2. Memory-Based Collaborative Filtering
  3. Model-Based Collaborative Filtering
  4. Deep Learning / Neural Network

Movie Recommendation System in Machine Learning-The Math

The concepts of Term Frequency and Inverse Document Frequency is in use in information retrieval systems and also content based filtering mechanisms. Hence, they are in use to determine the relative importance of a news item or movie etc.

Below is the equation to calculate the TF-IDF score:

Equation to calculate the TF-IDF score
Vector Space Model