Information processing device, category displaying method, program, and information storage medium

• US 10,134,073 B2
• Filed: 02/28/2013
• Issued: 11/20/2018
• Est. Priority Date: 07/31/2012
• Status: Active Grant

• Current Assignee: Rakuten Group, Inc.
• Original Assignee: Rakuten, Inc. (Rakuten Group, Inc.)
• Inventors: Umeda, Takashi
• Primary Examiner(s): Jalil, Neveen Abel
• Assistant Examiner(s): Baker, Irene
• Application Number:
  US14/367,239
• Publication Number:
  US 20140351094A1
• Time in Patent Office: 2,091 Days
• Field of Search: None
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

Users are enabled to select a category with more ease than in the past. An information processing device estimates, for a target category that is one of a plurality of categories which have a tree structure including a plurality of hierarchy levels and to each of which one or more objects belong, whether a user is knowledgeable about categories below the target category, based on an input from the user, and controls whether or not display means displays the categories below the target category, based on whether or not the user is estimated as being knowledgeable about the categories below the target category.

Claims

1. An information processing device, comprising:

at least one processor; and

at least one memory device that stores a plurality of instructions, which when executed by the at least one processor, causes the at least one processor to operate to;

obtain, based on a search criteria input by a user, a plurality of objects that are found as a result of a search with respect to a target category that is one of a plurality of categories which have a tree structure comprising a plurality of hierarchy levels and to each of which one or more objects belong;

calculate, in a first search, one of (a) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the B target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a first value which is variance of a count of found objects among the plurality of categories below the target category;

calculate, in a second search which is previous to the first search, one of (a) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a second value which is variance of a count of found objects among the plurality of categories below the target category;

calculate, for the target category, a purchase history count based on a purchase history of the user;

calculate, for the target category, a viewing history count based on a viewing history indicating objects of which details are output by detail displaying to the user;

calculate, for the target category, a favorites count based on a favorites list in which objects are registered to be viewed later by the user;

calculate a degree of use by an expression α

×

(the purchase history count)+β

×

(the viewing history count)+γ

×

(the favorites count) where α

, β

, and γ

are predetermined weighting coefficients and α

>

β

>

γ

;

calculate a weighted addition value by an expression c=S×

(the first value)+T×

(the degree of use), where S and T are predetermined weighting coefficients;

determine whether all of lower categories below the target category is to be displayed or not based on the weighted addition value and a degree of difference between the first value and the second value;

control displays to display all of the categories below the target category, based on the determination,

wherein, in the weighted addition of the first value and the degree of use, the weighting coefficient T is larger when the purchase history count, the viewing history count, or the favorites count out of the objects that belong to the target category is larger,

wherein the weighting coefficient S becomes smaller as the weighting coefficient T becomes larger.

2. The information processing device according to claim 1,

wherein each of the one or more objects belongs to one of lowest categories and to a plurality of categories above the one of the lowest categories.

3. The information processing device according to claim 1, wherein, the degree of use is calculated based on objects of which details are output by the detail displaying to the user;

objects purchased by the user; and

objects registered in a list of objects that are to be viewed later by the user, out of the objects that belong to the target category and that are not suggested to the user by the information processing device.

4. The information processing device according to claim 1, wherein the degree of use in is calculated based on objects of which details are output by the detail displaying to the user;

objects purchased by the user; and

objects registered in a list of objects that are to be viewed later by the user, out of objects that belong to the target category and that have fewer reviews written by other users than a review count threshold.

5. The information processing device according to claim 1,

wherein the plurality of objects comprise pieces of merchandise, and

wherein the degree of use is calculated based on a count of brand types of at least some of:

objects of which details are output by the detail displaying to the user;

objects purchased by the user; and

objects registered in a list of objects that are to be viewed later by the user, out of objects that belong to the target category.

6. The information processing device according to claim 1, wherein the degree of use is calculated based on a count of objects of which details are output to the user, a count of objects purchased by the user, and a count of objects registered in a list of objects that are to be viewed later by the user, out of objects that belong to the target category, and a judgment threshold which is set for each target category.

7. The information processing device according to claim 1, wherein the degree of use is estimated based on lengths of reviews input by the user about at least some of objects that belong to the target category.

8. The information processing device according to claim 1, wherein in the controlling, the display is controlled so that all categories below the target category are displayed when the weighted addition value is equal to or more than a given degree.

9. The information processing device according to claim 1, wherein in the controlling, the displaying of all categories below the target category is limited when the weighted addition value is less than a given degree.

10. The information processing device according to claim 8,

wherein, for each of a plurality of target categories that are on one of the plurality of hierarchy levels, the weighted addition value with respect to each category below the each of the plurality of target categories is calculated based on an input from the user, and

wherein, in the controlling, the display is allowed to display all categories below each target category for which the weighted addition value with respect to the lower categories is estimated to be equal to or more than a given degree, and forbids the display to display categories below each target category for which the weighted addition value with respect to the lower categories is estimated to be less than the given degree.

13. The information processing device according to claim 3, wherein the degree of use is calculated based on objects of which details are output by the detail displaying to the user;

objects purchased by the user; and

objects registered in a list of objects that are to be viewed later by the user, out of objects that belong to the target category and that have fewer reviews written by other users than a review count threshold.

14. The information processing device according to claim 9,

wherein the degree of use is calculated for each of a plurality of target categories that are on one of the plurality of hierarchy levels based on an input from the user, and

wherein in the controlling, the display is allowed to display all categories below each target category for which the weighted addition value with respect to the lower categories is estimated to be equal to or more than a given degree, and forbids the display to display categories below each target category for which the weighted addition value with respect to the lower categories is estimated to be less than the given degree.

15. The information processing device according to claim 1,

wherein a process of the calculating for one of the categories below the target category as a new target category is called recursively.

11. A category displaying method, comprising the steps of:

obtaining, based on a search criteria input by a user, a plurality of objects that are found as a result of a search with respect to a target category that is one of a plurality of categories which have a tree structure comprising a plurality of hierarchy levels and to each of which one or more objects belong;

calculating, in a first search, one of (a) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a first value which is variance of a count of found objects among the plurality of categories below the target category;

calculating, in a second search which is previous to the first search, one of (a) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a B count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a second value which is variance of a count of found objects among the plurality of categories below the target category;

calculating, for the target category, a purchase history count based on a purchase history of the user;

calculate, for the target category, a viewing history count based on a viewing history indicating objects of which details are output by detail displaying to the user;

calculate, for the target category, a favorites count based on a favorites list in which objects are registered to be viewed later by the user;

calculating a degree of use by an expression α

×

(the purchase history count)+β

×

(the viewing history count)+γ

×

(the favorites count) where α

, β

, and γ

are predetermined weighting coefficients and α

>

β

>

γ

;

calculating a weighted addition value by an expression c=S×

(the first value)+T×

(the degree of use), where S and T are predetermined weighting coefficients;

determining whether all of lower categories below the target category is to be displayed or not based on the weighted addition value and a degree of difference between the first value and the second value;

controlling displays to display all of the categories below the target category, based on the determination;

wherein, in the weighted addition of the first value and the degree of use, the weighting coefficient T is larger when the purchase history count, the viewing history count, or the favorites count out of the objects that belong to the target category is larger; and

wherein the weighting coefficient S becomes smaller as the weighting coefficient T becomes larger.

12. A non-transitory computer-readable information storage medium storing a plurality of instructions, wherein when executed by at least one processor, the plurality of instructions cause the at least one processor to:

obtain, based on a search criteria input by a user, a plurality of objects that are found as a result of a search with respect to a target category that is one of a plurality of categories which have a tree structure comprising a plurality of hierarchy levels and to each of which one or more objects belong;

calculate, in a first search, one of (a) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a first value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a first value which is variance of a count of found objects among the plurality of categories below the target category;

calculate, in a second search which is previous to the first search, one of (a) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a count of found objects belongs exceeds a given cutoff threshold;

(b) a second value which is the proportion, to a count of lower categories below the target category, of a count of lower categories which are below the target category and to each of which a ratio of a count of found objects belongs divided by a count of found objects belongs to the target category exceeds a given cutoff ratio; and

(c) a second value which is variance of a count of found objects among the plurality of categories below the target category;

calculate, for the target category, a purchase history count based on a purchase history of the user;

calculate, for the target category, a viewing history count based on a viewing history indicating objects of which details are output by detail displaying to the user;

calculate, for the target category, a favorites count based on a favorites list in which objects are registered to be viewed later by the user;

calculate a degree of use by an expression α

×

(the purchase history count)+β

×

(the viewing history count)+γ

×

(the favorites count) where α

, β

, and γ

are predetermined weighting coefficients and α

>

β

>

γ

;

calculate a weighted addition value by an expression c=S×

(the first value)+T×

(the degree of use), where S and T are predetermined weighting coefficients;

determine whether all of lower categories below the target category is to be displayed or not based on the weighted addition value and a degree of difference between the first value and the second value;

control displays to display all of the categories below the target category, based on the determination;

wherein, in the weighted addition of the first value and the degree of use, the weighting coefficient T is larger when the purchase history count, the viewing history count, or the favorites count out of the objects that belong to the target category is larger; and

wherein the weighting coefficient S becomes smaller as the weighting coefficient T becomes larger.