Posted by Wei Wei, Developer Advocate

Recommendation systems, often called recommenders as well, are a type of machine learning systems that can give users highly relevant suggestions based on the user's interests. From recommending movies or restaurants, to highlighting news articles or entertaining videos, they help you surface compelling content from a large pool of candidates to your users, which boosts the likelihood your users interact with your products or services, broadens the content your users may consume, and increases the time your users spend within your app. To help developers better leverage our offerings in the TensorFlow ecosystem, today we are very excited to launch a new dedicated page that gathers all the tooling and learning resources for creating recommendation systems, and provides a guided path for you to choose the right products to build with.

While it is relatively straightforward to follow the Wide & Deep Learning paper and build a simple recommender using the TensorFlow WideDeepModel API, modern large scale recommenders in production usually have strict latency requirements, and thus, are more sophisticated and require a lot more than just a single API or model. The generated recommendations from these recommenders are typically a result of a complex dance of many individual ML models and components seamlessly working together. Over the years Google has open sourced a suite of TensorFlow-based tools and frameworks, such as TensorFlow Recommenders, which powers all major YouTube and Google Play recommendation surfaces, to help developers create powerful in-house recommendation systems to better serve their users. These tools are based upon Google’s cutting-edge research, extensive engineering experience, and best practices in building large scale recommenders that power a number of Google apps with billions of users.

You can start with the elegant TensorFlow Recommenders library, deploy with TensorFlow Serving, and enhance with TensorFlow Ranking and Google ScaNN. If you encounter specific challenges such as large embedding tables or user privacy protection, you will be able to find suitable solutions to overcome them from the new recommendation system page. And if you want to experiment with more advanced models such as graph neural networks or reinforcement learning, we have listed additional libraries for you as well.

This unified page is now the entry point to building recommendation systems with TensorFlow and we will keep updating it as more tools and resources become available. We’d love to hear your feedback on this initiative, please don’t hesitate to reach out via the TensorFlow forum.