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.