Build a Collaborative Filtering Recommender System in Python

For this project, we are developing a recommendation system that recommends movies based on users' preferences. We are going to use hybrid methods such as collaborative filtering, content-based filtering, and LightFM. The task is to predict and recommend movies based on the user's preferences and what he is likely to love!

Project Outcomes

✓The system is designed to scale efficiently for platforms with millions of users and items
✓offering real
✓time personalization for better recommendations and increased conversions in various domains
✓including e
✓commerce and media streaming.
✓The system enhances user participation by utilizing recommendation features similar to the Netflix or Amazon recommendation system.
✓It improves searching for content and allows the users to move through the other items
✓such as listening to music on Spotify.
✓The model is scalable for platforms with millions of users and items since it works well with large datasets.
✓Personalized suggestions show higher engagement
✓similar to the YouTube auto
✓suggestion feature.
✓While collaborative filtering is more accurate
✓errors are reduced by combining it with content
✓based systems
✓especially when data is small or new.
✓By recommending products
✓the system can increase sales and conversion in an e
✓commerce environment.
✓This helps to manage inventories by estimating customers' behaviors and consequently setting appropriate inventory orders.
✓To address the cold start problem
✓models are developed that use both collaborative and content
✓based filtering for new users and new items.
✓It has great benefits to marketers and advertisers when deciding on marketing and products to advertise and market.
✓The use of real
✓time permits the adaptation of the system by these rapidly changing preferences.

Requirements:

  • →A general understanding of Python programming and usage of data analysis tools such as pandas, and NumPy.
  • →Knowledge of concepts in machine learning especially collaborative filtering and content-based filtering.
  • →Pre-knowledge of recommendation systems along with the assessment methods such as RMSE and MAE.
  • →Knowledge of Python libraries especially LightFM, scikit-learn, and Surprise.
  • →Understanding of a cosine similarity and its usage in an item-based collaborative filtering.
  • →Understanding of how to take care of and manage data and some of the things that need to be done to prepare the data such as handling missing values.
  • →Knowledge of similar validation methods such as cross-validation and the understanding of metrics that describe performance.

Project Description

The project develops a movie recommendation system based on different techniques- collaborative filtering, content-based filtering, and hybrid methods with LightFM.

We first clean and prepare two datasets- one for user ratings and another for movie metadata; handle the missing values in the datasets, and then apply Singular Value Decomposition (SVD) to perform collaborative filtering in predicting ratings based on user input. LightFM is then used to combine collaborative methods and content-based ones for improving recommendation quality that takes into account user behavior and movie features. Item-based collaborative filters have also been implemented for finding movies similar to each other using cosine similarity.

Finally, weighted ratings are computed on movies to rank them based on average ratings considering also the number of votes they have received. The end output would thus be a personalized movie recommendation system for the users to find enjoyable movies.

Build a Collaborative Filtering Recommender System in Python

Learn how to create a personalized movie recommendation system using SVD, LightFM, collaborative filtering, and weighted ratings for accurate and scalable suggestions.

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