Overview of Building Recommender System with Deep Learning

 
  • Recommendation Engine involves personalised 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 the 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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