Recommender Systems Project : TikTok videos

Project Overview

This project focuses on building and evaluating recommendation systems using various techniques. The goal is to provide personalized recommendations to users based on their preferences and interactions. The notebook explores three main approaches: Content-Based Filtering, Alternating Least Squares (ALS), and K-Nearest Neighbors (KNN).

Techniques Used

1. Content-Based Filtering

Content-Based Filtering recommends items to users based on the similarity between items and the user's preferences. This approach was chosen because:

• It works well when sufficient metadata about items is available.
• It does not require user interaction data, making it suitable for cold-start scenarios for users.
• It provides explainable recommendations by highlighting shared features between recommended items and user preferences.

2. Alternating Least Squares (ALS)

ALS is a collaborative filtering technique that uses matrix factorization to predict user-item interactions. This method was selected because:

• It handles sparse datasets effectively, which is common in recommendation systems.
• It can capture latent factors that influence user preferences.
• It is scalable and suitable for large datasets.

3. K-Nearest Neighbors (KNN)

KNN is a memory-based collaborative filtering method that recommends items based on the similarity between users or items. This approach was included because:

• It is simple to implement and interpret.
• It can provide high-quality recommendations when user-item interaction data is dense.
• It complements ALS by offering a different perspective on collaborative filtering.

Justification for Approach Selection

The combination of these techniques allows for a comprehensive evaluation of recommendation strategies. Content-Based Filtering ensures recommendations are tailored to user preferences based on item features. ALS leverages collaborative filtering to uncover hidden patterns in user-item interactions. KNN provides a straightforward and interpretable method for generating recommendations. Together, these methods address various challenges in recommendation systems, such as cold-start problems, scalability, and sparsity.

Conclusion

These notebooks demonstrate the implementation and evaluation of three recommendation system techniques, highlighting their strengths and trade-offs.