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Abstract

A method and apparatus are disclosed for recommending television programming using decision trees. Inductive principles are utilized to identify a set of recommended programs that may be of interest to a particular viewer, based on past viewing behavior. A user's viewing history is monitored and the shows that are actually watched by a user (positive examples) and the shows that are not watched by the user (negative examples) are analyzed. For each positive and negative program example (i.e., programs watched and not watched), a number of program attributes are classified in the user profile. The various attributes are then positioned in a hierarchical decision tree based on a ranking of the entropy of each attribute. Each node and sub-node in the decision tree corresponds to a given attribute from the user profile. Each leaf node in the decision tree corresponds to either a positive or negative recommendation for a program mounted at the corresponding leaf node. The decision tree is built using a decision tree process that implements a 'top-down divide and conquer' approach. The decision tree can thereafter be applied to an electronic program guide to make program recommendations.

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First Claim