Evaluation Prediction Using Multi-Source User Data
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Solution Overview
Problem
Existing techniques for predicting user evaluations based on linguistic expressions face accuracy issues due to insufficient quantities of direct evaluation expressions from individual users.
Innovation Solution
An information processing apparatus and method that extracts evaluation information from linguistic expressions, identifies the type of evaluation, and predicts user evaluations by combining evaluation information from the user of interest and other users, using parameter estimation and weighted averaging to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evaluation information is extracted only from the user of interest, then the prediction system is simple, but the prediction accuracy deteriorates due to insufficient quantities of evaluation expressions
Solution Approach 1:
The patent combines evaluation information from multiple sources: the user of interest and other users. The evaluation prediction section integrates first-type evaluation information (from the user of interest) and second-type evaluation information (from other users) to predict content evaluations. This merging of data sources increases the quantity of evaluation expressions available for prediction, thereby improving accuracy without requiring a fundamentally complex system architecture.
Solution Approach 2:
The system processes two types of evaluation information through a unified evaluation prediction section. The same extraction and prediction mechanisms handle both first-type information (user of interest evaluating content) and second-type information (other users evaluating content), making the system multi-functional while maintaining structural simplicity.
2Measurement precision
If evaluation information from multiple users is integrated, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent segments evaluation information into two distinct types: first-type information (evaluation by the user of interest) and second-type information (evaluation by other users). This segmentation allows the system to process different data sources through dedicated but similar pathways, reducing the overall processing difficulty compared to handling undifferentiated multi-user data.
Solution Approach 2:
The evaluation prediction section acts as an intermediary that standardizes and integrates evaluation information from multiple users. It transforms diverse evaluation expressions into a unified format suitable for prediction, mediating between the raw data from multiple sources and the final prediction output, thereby simplifying the data processing task.
Data Source
AI summary
Disclosed herein is an information processing apparatus including an evaluation information extraction section configured to extract evaluation information including an object targeted to be evaluated and an evaluation of the object targeted to be evaluated from a linguistic expression given as information expressed linguistically by a user of interest; an identification section configured to identify whether the evaluation information is of a first type regarding content or of a second type regarding another user; and an evaluation prediction section configured to predict the evaluation by the user of interest regarding the content, based on the evaluation information of the first type given by the user of interest and on the evaluation information given by the other user in the evaluation information of the second type given by the user of interest.