Introduction

This blog post will introduce you to the fundamental concepts of building a recommendation system using Python. We will discuss two popular techniques: Collaborative Filtering and Content-Based Filtering.

Collaborative Filtering

Collaborative Filtering recommends items to a user based on the preferences of other users who have similar taste. It mainly relies on the user-user and item-item similarity.

User-User Similarity

User-user similarity is calculated by comparing the preferences of two users. Cosine similarity and Pearson correlation are two common methods to measure user-user similarity.

Item-Item Similarity

Item-item similarity is calculated by comparing the ratings of two items across all users. Cosine similarity is also commonly used to measure item-item similarity.

Content-Based Filtering

Content-based filtering recommends items to a user based on the properties of an item, such as its attributes or content. It analyzes user’s preferences by comparing the similarity between the content of the items a user has liked and the content of other items.

Textual Content Analysis

Textual content analysis is a common method used in content-based filtering. It involves converting textual data into numerical representations and calculating the similarity between items based on their numerical representations.

Conclusion

Both Collaborative Filtering and Content-Based Filtering have their strengths and weaknesses. Collaborative Filtering can handle sparse data well but may suffer from the cold start problem. Content-Based Filtering can overcome the cold start problem but may not perform well with complex items or large data sets.

Next Steps

To dive deeper into recommendation systems, you can start by implementing these techniques using popular Python libraries such as Scikit-learn and TFX (TensorFlow Extended). You can also explore more advanced techniques like Hybrid Filtering and Matrix Factorization.