Practical Recommender Systems

• Practical introduction to recommender system algorithms
• Collaborative and content-based filtering
• Creating individual recommendations from visitor data
• Real-world examples of recommender systems

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Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance.

Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions.

Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.

Practical Recommender Systems goes behind the curtain to show you how recommender systems work and, more importantly, how to create and apply them for your site.

After you've covered the basics of how recommender systems work, you'll discover how to collect user data and produce personalized recommendations.

Next, you'll learn how and where to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, this hands-on guide covers scaling problems and other issues you may encounter as your site grows.

This book assumes you're comfortable reading code in Python and have some experience with databases.