Recommendation System with Machine Learning - XenonStack

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.
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