Household Relationship Estimation via Multi-Parameter Analysis
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Solution Overview
Problem
Current technologies only estimate the presence or absence of a spouse or child as a user attribute, failing to provide a detailed understanding of the household situation to which a user belongs.
Innovation Solution
An information processing system and method that identifies households by acquiring user data such as surname and street address, and estimates the relationship between households based on attributes like surname, telephone contact frequency, gift exchange, age difference, shared friends, and gender similarity to determine relationships like parent-child, sibling, or neighbor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user attributes (spouse/child presence) are simply estimated, then the system is easy to operate, but the household situation cannot be grasped in detail
Solution Approach 1:
The system segments the relationship estimation process into multiple independent parameter evaluations (surname similarity, street address proximity, age difference, gender, contact frequency, gift exchange). Each parameter is evaluated separately and combined to form the overall relationship type, making the complex estimation task manageable and systematic.
Solution Approach 2:
The system changes from binary attribute estimation (spouse/child presence) to multi-dimensional parameter analysis. By introducing multiple parameters with different weights and thresholds, the system can distinguish between various relationship types (parent-child, sibling, neighbor, friend) based on the pattern of parameter values.
2Measurement precision
If multiple parameters are used to estimate relationship types, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system merges multiple parameter evaluation results into a unified relationship type classification. By combining surname similarity, address proximity, age difference, gender, contact frequency, and gift exchange data, the system achieves comprehensive relationship identification that cannot be obtained by any single parameter alone.
Solution Approach 2:
The relationship estimation system serves multiple functions simultaneously: it identifies relationship types, estimates household composition, and provides basis for service recommendations. This multi-functionality justifies the system complexity by delivering multiple valuable insights from a single integrated analysis.
Data Source
AI summary
To grasp a situation of a household to which a user belongs in more detail, a household identification included in an information processing system acquires household information indicating a first household and a second household each including one or a plurality of users living together. Household relationship estimation unit included in the information processing system estimates a type of a relation between the first household and the second household based on an attribute of a user belonging to the first household and an attribute of a user belonging to the second household.