PREFERENCE ANALYZING SYSTEM

• US 20170011421A1
• Filed: 07/29/2014
• Published: 01/12/2017
• Est. Priority Date: 07/29/2014
• Status: Abandoned Application

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: FUJITA, Marina, AIZONO, Toshiko, ARA, Koji
• Application Number:
  US15/121,166
• Publication Number:
  US 20170011421A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

The present invention aims to extract change factors that raise and lower evaluation with respect to a product based on a purchase history of an individual. A preference analyzing system according to the present invention learns, from a purchase history, a preference model that evaluates purchase preference of an individual, calculates correlation between a feature quantity representing an attribute of a product and a mixed attribute that can raise and lower evaluation of the product, and extracts other product attributes that change the mixed attribute (see FIG. 6).

Claims

1. A preference analyzing system that analyzes purchase preference of an individual, comprising:

a learner that learns purchase preference of the individual with respect to a product based on purchase history data that describes a history of the product purchased by the individual, and that stores tree structure data representing a learning result in a storage unit;

a classifier that extracts, from the learning result by the learner, a tendency in which evaluation by the individual with respect to the product is raised and lowered according to an attribute of the product, that classifies the extracted tendency based on a raising/lowering pattern thereof and that identifies, from among the classified raising/lowering patterns, a mixed pattern in which the pattern raising evaluation by the individual with respect to the product and the pattern lowering evaluation by the individual with respect to the product are mixed;

a feature quantity analyzer that extracts, as a vector of the attribute, a feature quantity of the product corresponding to each leaf node of the tree structure data; and

a change factor extractor that calculates correlation between the mixed pattern and the vector corresponding to each of the leaf nodes, thereby identifying, as a change factor, the attribute that raises or lowers evaluation by the individual with respect to the product having the attribute generating the mixed pattern, and that outputs a result thereof.

2. The preference analyzing system according to claim 1,

wherein the learner learns coefficients of a plurality of evaluation functions that evaluate the purchase preference, and learns a structure of the tree structure data so that the purchase history data is evaluated by the evaluation function that is optimum for evaluating the purchase history data.

3. The preference analyzing system according to claim 2,

wherein the evaluation function is a function that totals, for each of the attributes, numerical values obtained by multiplying numerical values representing the attribute by the coefficients,

wherein the tree structure data classifies a purchase history of the product described by the purchase history data into any one of the leaf nodes, and evaluates the purchase history classified into the leaf node by the evaluation function associated with the leaf node, and

wherein the classifier obtains, for each of the leaf nodes, the coefficient of the leaf node by which the same attribute is multiplied, and when the coefficient increasing an evaluation value and the coefficient decreasing an evaluation value are mixed in each of the obtained coefficients, the classifier determines that the attribute is the attribute that generates the mixed pattern.

4. The preference analyzing system according to claim 2,

wherein the feature quantity analyzer uses, as an element value of the vector corresponding to each of the leaf nodes, a rate between the number of purchase histories of the product classified into the leaf node by the tree structure data and the number of the purchase histories classified into the leaf node and having the attribute.

5. The preference analyzing system according to claim 2,

wherein the feature quantity analyzer uses, as an element value of the vector corresponding to each of the leaf nodes, orate between the total number of purchase histories of the product classified by the tree structure data and the number of the purchase histories classified into each of the leaf nodes by the tree structure data and having the attribute.

6. The preference analyzing system according to claim 1,

wherein the purchase history data describes the histories associated with a plurality of the individuals,

wherein the preference analyzing system includes a totalizer that totalizes processing results by at least any one of the learner, the classifier, the feature quantity analyzer, and the change factor extractor for the plurality of the individuals, and

wherein the preference analyzing system outputs a totalizing result by the totalizer.

7. The preference analyzing system according to claim 6,

wherein the totalizer totalizes classification results of the tendencies by the classifier for the plurality of the individuals, and outputs the totalizing result.

8. The preference analyzing system according to claim 7,

wherein the totalizer totalizes identification results of the change factors by the change factor extractor for the plurality of the individuals, and

wherein the preference analyzing system outputs a result obtained in such a manner that the totalizer totalizes identification results by the change factor extractor for the plurality of the individuals, as the change factors that raise and lower evaluation by the plurality of the individuals with respect to the product.

9. The preference analyzing system according to claim 8,

wherein the preference analyzing system uses, as the attribute, a price of the product, and

wherein the preference analyzing system outputs the change factors that raise and lower evaluation by the plurality of the individuals with respect to the price of the product based on a totalizing result by the totalizer.

10. The preference analyzing system according to claim 9,

wherein when evaluation by the plurality of the individuals with respect to the price of the product is raised and lowered according to a combination of the plurality of the change factors, the preference analyzing system outputs the combination.

11. The preference analyzing system according to claim 8,

wherein the preference analyzing system uses, as the attribute, a store form in which the individual purchases the product, and

wherein the preference analyzing system outputs the change factors that increase and decrease purchase frequencies of the plurality of the individuals in each of the store forms based on a totalizing result by the totalizer.

12. The preference analyzing system according to claim 8,

wherein the preference analyzing system statistically estimates an amount in which evaluation by the plurality of the individuals with respect to the product is raised and lowered by adjusting the change factors based on a result obtained in such a manner that the totalizer totalizes classification results by the classifier, and outputs the estimation result.

13. The preference analyzing system according to claim 8,

wherein the preference analyzing system uses, as the attribute, at least any one of a time period and a day of the week at purchasing the product by the individual, and

wherein the preference analyzing system determines whether each of the time periods or each of the days of the week corresponds to the change factors that increase and decrease a purchase frequency of the individual, and outputs the result.

14. The preference analyzing system according to claim 13,

wherein the preference analyzing system transmits a message that promotes purchase of the product with respect to the individual in the time period or the day of the week extracted as the change factor that increases the purchase frequency of the individual.