Method and system for recommending products to consumers by induction of decision trees

• US 20070244747A1
• Filed: 04/14/2006
• Published: 10/18/2007
• Est. Priority Date: 04/14/2006
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Nikovski, Daniel
• Application Number:
  US11/404,940
• Publication Number:
  US 20070244747A1
• Time in Patent Office: Days
• US Class Current: 705/14.530
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

A method and system recommend a product to a consumer. A purchasing history of a consumer is represented by an adjacency lattice stored in a memory. Training examples are extracted from the adjacency lattice, and a decision tree is constructed using the training examples. A size of the decision tree is reduced, and the reduced size decision tree is searched for a recommendation of a product to the consumer.

Claims

1. A computer implemented method for recommending a product to a consumer, comprising the steps of:

representing a purchasing history of a consumer as an adjacency lattice;

extracting training examples from the adjacency lattice;

constructing a decision tree using the training examples;

reducing a size of the decision tree to a reduced size decision tree; and

searching the reduced size decision tree for a recommendation of a product to the consumer.

2. The method of claim 1, in which the extracting is according to a predetermined threshold.

3. The method of claim 1, in which the purchasing history includes items, each item having an identification and an item-set.

4. The method of claim 1, in which the adjacency lattice is in a form of a directed acyclic graph.

5. The method of claim 1, in which the decision tree includes a root node, intermediate nodes for storing attributes, and leaf nodes for storing purchasing decisions.

6. The method of claim 1, in which the constructing uses machine learning processes.

7. The method of claim 1, in which the decision tree is a binary tree.

8. A system for recommending a product to a consumer, comprising the steps of:

a memory configured to store an adjacency lattice representing a purchasing history of a consumer;

means for extracting training examples from the adjacency lattice;

means for constructing a decision tree using the training examples;

means for reducing a size of the decision tree to a reduced size decision tree; and

means for searching the reduced size decision tree for a recommendation of a product to the consumer.

9. The system of claim 8, in which the purchasing history includes items, each item having an identification and an item-set.