Information Processing Device for Precision Content Recommendation
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Solution Overview
Problem
Current collaborative filtering techniques in content recommendation systems often fail to accurately match user preferences by relying on general user evaluations rather than personalized preferences, leading to less precise recommendations.
Innovation Solution
An information-processing device and system that accepts user instructions to select and recommend content based on evaluations from users with similar preferences, utilizing a network-connected server and gaming system to extract and present non-overlapping, highly recommended content that aligns with the user's interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general user evaluations are used for content recommendation, then the system can process a large volume of data, but the recommendation precision decreases
Solution Approach 1:
The patent segments users into different preference groups based on their evaluation patterns. Instead of treating all users uniformly, the system divides the user base into segments with similar tastes, then uses evaluations from users within the same segment to make recommendations. This segmentation allows the system to maintain high recommendation precision by focusing on relevant user feedback while still processing large volumes of data across multiple segments.
2Measurement precision
If recommendations are based on users with similar preferences, then recommendation precision improves, but the complexity of user analysis increases
Solution Approach 1:
The system implements feedback mechanisms where user evaluations are continuously collected and used to refine preference groupings. The feedback loop allows the system to learn from user interactions and automatically adjust which users are grouped together based on their evaluation patterns. This reduces the manual complexity of analyzing user preferences while maintaining high recommendation precision through data-driven segmentation.
Solution Approach 2:
The system performs automatic user segmentation and preference analysis without requiring manual intervention. Algorithms automatically identify users with similar preferences based on their evaluation behavior and use this information to generate recommendations. This self-service approach reduces system complexity by automating the sophisticated user analysis process rather than requiring complex manual configuration.
3Adaptability or versatility
If the system processes evaluations from many users, then the diversity of recommendations increases, but the accuracy of matching user preferences decreases
Solution Approach 1:
The patent applies segmentation by organizing users into preference-based segments and making recommendations within those segments rather than across the entire user base. This ensures that recommendations are diverse enough to be interesting while remaining accurate to the user's specific preferences. The system processes evaluations from many users but only uses those from the same preference segment, maintaining both diversity and accuracy.
Data Source
AI summary
A first accepting unit (111) of an exemplary server device (100) accepts from a user an instruction to select one of plural selection targets. A user selecting unit (112) selects, from among plural users other than the user, another user who has recommended or evaluated the selection target, selection of which has been accepted by the first accepting unit (111). A selection target selecting unit (113) selects one or more selection targets that the other user selected by the user selecting unit (112) has recommended or evaluated. A presenting unit (114) presents, to the user, information on the one or more selection targets selected by the selection target selecting unit (113).