Movie-Recommendation-Engine

Recommendation systems are becoming increasingly important in today’s extremely busy world. People are always short on time with the myriad tasks they need to accomplish in the limited 24 hours. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources.

These systems estimate the most likely product that consumers will buy and that they will be interested in. Netflix, Amazon, and other companies use recommender systems to help their users find the right product or movie for them.

Types of Recommendation Engines

• Demographic Filtering:The recommendations are the same for every user. They are generalised, not personalised. These types of systems are behind sections like “Top Trending”.
• Content-based Filtering:These suggest recommendations based on the item metadata (movie, product, song, etc). Here, the main idea is if a user likes an item, then the user will also like items similar to it.
• Collaboration-based Filtering:These systems make recommendations by grouping the users with similar interests. For this system, metadata of the item is not required.

Contents of Datasets

• Movies Dataset: This dataset files contains contains all the metadata information about the movie.
• Credits Dataset: This Dataset file contains the information like name and id of the movie, budget, languages in the movie that has been released, etc.

Steps in Making the Engine

• Perform Exploratory Data Analysis (EDA) on the data. Exploratory Data Analysis (EDA) is an approach to analyse the data using visual techniques. It is used to discover trends, patterns, or to check assumptions with the help of statistical summary and graphical representations.  This forms the basis of the major part in where the engine is trained and made well versed with the dataset.
• Build the recommendation system In this step of development, the recommendation engine is further improved to make its recommendation very precise.
• Get recommendations This is where this piece of code becomes into an actual working recommendation engine.

Output 👇

All development stages for this repository are completed!

Snippet for Getting Recommendations

  print("Recommendations for Interstellar") <br>
  print(get_recommendations("Interstellar", cosine_sim2)) <br>
  print() <br>

All development stages for this repository are completed!