Association Rule Learning: Background Association Rule Learning (also called Market Basket Analysis) is a practical and highly interpertable starting place for implementing the first recommendation algorithm for your business. Association rules, or strong relationships between variables in a dataset, can be mined from historical data using an appropriate algorithm. Those rules can then be leveraged to effectively predict future user behavior. Association rules are commonly applied to assist with marketing decisions such as selecting users for a specific ad campaign, recommending personalized services, or smart product up-selling at checkout.

Recommendation Algorithms: Background Popularized by Amazon in the 1990s, recommendation algorithms have become a staple of business analytics. From shopping add-ons to information sharing via contact the influence of recommendation algorithms is undeniable. Under the hood, recommendation algorithms use both implicit (i.e number of times the item is purchased) and explicit (i.e star ratings) user feedback to determine the strength of association for a recommendation. I want to first define two main types of recommendation algorithms; content-based vs collaborative filtering.