Xenonstack Recommends

Recommendation System with Machine Learning

Acknowledging Data Management
          Best Practices with DataOps

Subscription

Table of content

What is Recommendation System

A Recommendation System has defined as a system that is proficient of predicting the future choice of a set of items for a user and advocate the top items. One primary reason why we need a recommender system in current society is that people have too many opportunities to use from due to the ubiquity of the Internet. In the past, people were used to shopping in a physical store, with limited access to items. But nowadays, everything is available online with a variety of options so that people as choose as per their taste and requirement.  Although the number of available information increased, a new problem occurred as people had a hard time choosing the items they actually want to see. Hence, the recommender system comes into play.
  • 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.
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.

Download the Use Case

Download Now and Get Access to the detailed Use Case

XenonStack Cyber Security Solution Image

Related UseCases

Request for Services

Find out more about How your Enterprise can Streamline Data Operations and enable effective Management