Would You Like Fries with That?
Have you ever placed more items than you had originally anticipated in your Amazon cart? Maybe you accidentally spent your entire Sunday Funday mindlessly watching YouTube videos on auto play. These sites utilize a data science / machine learning technology called a Recommender System. Recommender systems are very common among e-commerce sites and have proven to be valuable for companies as they help to increase revenue and customer satisfaction.
At a high level, a recommender system provides suggestions as to what you would be interested in based on your past preferences. There are two types of recommender systems, 1) Content-based and 2) Collaborative filtering.
An example of how a Content-based recommender system works is that data is collected related to the product and its features. Those features are then compared to other product features to determine which products are similar. The system can then suggest other similar products.
An example of how a Collaborative filtering recommender system works is that data is collected related to the ratings that a user gives to an item. That data is used to predict how that user will rate other items. Using these predicted ratings, the system can then suggest other items that it thinks the user will enjoy.
Now, how can we apply this technology to help with my indecisiveness towards food??
By: Monica Kay Royal