Deep learning-based search and recommendation systems using TensorFlow

Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow. You'll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client.

Recommendation systems are all around us. Ecommerce companies like Amazon recommend goods that we are likely to buy based on our past behavior. Netflix suggests what videos we should watch. Pandora even builds personalized music streams, based on what we are likely to listen to. Almost every website has a recommendation system based on user browsing history, past purchases, past searches, and preferences. It turns out most existing recommendation systems are based on three paradigms: collaborative filtering (CF) and its variants, content-based recommendation engines, and hybrid recommendation engines that combine content-based and CF or exploit more information about users in content-based recommendation. Recommendation systems must be accurate, able to handle sparse data, able to recommend items that have never been rated (cold start), and scalable. The memory-based CF systems are highly scalable but may suffer from cold start and data sparsity problems. Model-based CF systems such as the Naïve Bayes recommendation engine often outperform memory-based CF systems with respect to accuracy. Matrix factorization-based recommendation systems (the most advanced systems) have the best accuracy but may suffer from performance degradation issues at extreme scale. So what’s the solution? Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow, including model management and scaling. You’ll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client. When users search for products, the system ranks search results based on purchase behavior and other possible sources of data, such as browsing history, domain catalog, and social traits/behavior analysis of the user.