XenonStack Use Case Recommendation System with Machine Learning

What is Recommendation System

• Recommendation Engine involves personalized informational flows independently for each user, takes into account the behavior of all users of a service. Traditional Approaches involve Content-based filtering and Collaborative Filtering.
• Recent approaches of Recommendation Systems involve Multi-Armed Bandit algorithms maximizing click-through rates.
• Deep Neural Networks approach to solving candidate generation and ranking treating recommendation as extreme multiclass classification.
• Deep Learning approach for offering scalability and higher resource utilization.
• Other approaches include Google Cloud Compute Engine, Azure ML studio, etc.

Major Challenges in Building Recommendation System

• To identify products sold the most and are in high demand in that area providing maximum profits.
• To identify the products often bought together.
• To maintain a Demand-Supply ratio to get maximum profit.

Solution Offerings for Building Recommendation System

• Automated-Recommendation agent that takes customer information and product information as input and gives recommendations as output not only made for customers but the industry also.
• Autobot helps in recommending the products, replacements, etc. to increase profits. Finds similar products using similarity algorithms.
• Transaction history of the customers and details like, products sold out the most, maximum profit on given items, the best time frame for a product getting sold, products that sold in pairs, groups.
• Suggest the products that are sold together using association rules.
• Find the best profit group of items by calculating the profit from each group of frequently sold things and put them together in the store.

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.

• 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.