What are the Hypotheses Behind the Main Solutions of Recommender System
Boost sales with machine learning and statistics powered recommender system
To buy, or not to buy: that is the question.
If you were a manager of a grocery store, what products customers never bought but may like? If you run a restaurant, what is the food clients never ordered but may like? If you worked in sales or marketing, what are the books, movies, software, insurance, or t-shirt users never purchased but may like?
Why business needs recommender systems
Boosting sales to the next level by recommending the right products to the right customers, a hybrid recommender system is a promising approach.
Items bought vs Never bought but may like: the recommender system was born with the mission to figure out the answers. Powered by machine learning and statistics, models are able to discover the hidden association between products from historical orders and the underlying patterns in latent features using the users-items matrix.
