Building association rules using machine learning

Association rules is a data mining technique where the goal is identifying relationships between various entities within a given dataset by identifying entities that occur frequently together. Association rules are useful in making new item recommendations based on the relationship between existing items that frequently appear together. In data mining association, rules are implemented using a series of if-then-else statements that help show the probability of relationships between entities. The association rules technique is widely used in recommender systems, market basket analysis, and affinity analysis problems.

Collaborative filtering using alternating least squares

In machine learning, collaborative filtering is more commonly used for recommender systems. A recommender system is a technique that's used to filter information by considering user preference. Based on user preference and taking into consideration their...