Guide to Building Recommendation System with Tensorflow
- Recommendation System with Tensorflow builds a recommendation engine capable of learning from specific positive and negative feedback, allowing for arbitrary TensorFlow graphs used as representation functions and loss functions.
- It provides reasonable defaults for representation functions and loss functions. It packs as many Machine Learning buzzwords into a Medium post as possible.
- TensorFlow, initially developed by Google, is an open source tool that to build, optimize, and distribute large, arbitrary Machine Learning system.
- Machine Learning process expressed as a ‘graph’ showing data flow through the system, graphs visualized using TensorBoard.
Steps to build Recommendation System using Tensorflow –
- Transform input data into feature tensors for easy embedding.
- Transform user feature tensors into user representations function.
- Transform a pair of representations into a prediction.
- Transform predictions and truth values into a loss value function.
