Model Training for Unknown Facility Domain Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information suggestion techniques face challenges in providing high-performance suggestions at unknown introduction destination facilities due to domain shift issues, where machine learning models trained on one facility do not perform well in another, leading to decreased prediction accuracy and lack of suitable models for unknown domains.
Innovation Solution
An information processing method that represents and trains multiple models based on the characteristics of various facilities, ensuring improved prediction performance across different domains by simulating and weighting data to cover a range of possible characteristics, thereby generating diverse models suitable for unknown introduction destination facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained on data collected at a specific facility, then the prediction accuracy at that facility is improved, but the prediction accuracy at unknown introduction destination facilities deteriorates
Solution Approach 1:
The patent applies preliminary action by training multiple models in advance, each specialized in representing different facility characteristics (e.g., age distribution, user behavior patterns) before the actual introduction destination is known. This allows the system to prepare diverse models that can adapt to various unknown facilities without requiring retraining, thereby maintaining high prediction accuracy across different domains while improving robustness against domain shift.
2Reliability
If multiple models are trained to cover different facility characteristics, then the robustness against domain shift is improved, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the overall model into multiple specialized sub-models, where each model is trained to represent a specific facility characteristic or user segment. Instead of training one large complex model that attempts to handle all possible facilities, the system segments the prediction task into multiple simpler, specialized models that can be selected or combined based on the introduction destination's characteristics, thereby reducing individual model complexity while improving overall system robustness.
Data Source
AI summary
There is provided an information processing method, an information processing apparatus, and a program capable of preparing a high performance model for an unknown introduction destination facility even in a case where a domain of the introduction destination facility is unknown at a step of training a model.An information processing method executed by one or more processors, in which the one or more processors include representing characteristics of a plurality of second facilities different from a first facility where a dataset, which is used for a training of a model that predicts a behavior of a user on an item, is collected, and training a plurality of the models such that prediction performance at each of the second facilities is improved according to the characteristics of each of the second facilities.