Association rules for learning recommendation system
Although this method is not used often in many commercial recommendation systems, association rules learning is certainly a method worth knowing about because of historical data reasons, and it can be employed to solve a wide range of problems in real-world examples. The main concept of this method is to find relationships among items based on some statistical measure of the occurrences of the items in the database of transactions T (for example, a transaction could be the movies seen by a user i or the products bought by i). More formally, a rule could be {item1,item2} => {item3}, that is, a set of items ({item1,item2}) implies the presence of another set ({item3}). Two definitions are used to characterize each X=>Y rule:
Support: Given a set of items X, the support supp(X) is the portion of transactions that contains the set X over the total transactions.
Confidence: It is the fraction of transactions that contains the set X that...
