Recommendation System

Overview

This skill implements collaborative and content-based recommendation systems with matrix factorization techniques to predict user preferences, increase engagement, and drive conversions through personalized item suggestions.

When to Use

• Developing recommendation features to improve user engagement and retention
• Implementing personalized product suggestions to increase sales and conversion rates
• Building hybrid recommendation systems that combine collaborative and content-based approaches
• Analyzing and optimizing recommendation coverage, diversity, and accuracy
• Handling sparse user-item interaction matrices and cold start scenarios
• Running A/B tests to measure the impact of recommendation algorithms on business metrics

Approaches

• Collaborative Filtering: Users similar to you liked X
• Content-based: Items similar to what you liked