Recommender List With Per-Genre Evaluation Scores
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Solution Overview
Problem
Existing content recommendation systems face challenges in accurately providing users with desired content, as they rely solely on buying history and may not account for user preferences accurately, leading to limited recommendations and dissatisfaction.
Innovation Solution
An information processing device and method that utilize a recommender list with evaluation scores per genre to select and send recommended content, where the evaluation scores are updated based on user feedback, and the list is generated from population information formed by social network relationships, ensuring content is tailored to user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content recommendation is based solely on buying history, then the recommendation service can be implemented, but the recommended content is limited and may not accurately reflect user preferences
Solution Approach 1:
The patent combines multiple information sources including buying history, social network relationships, and user evaluations to create a comprehensive recommendation system. This merging of diverse data sources expands the pool of recommended content while improving accuracy by cross-validating preferences through multiple channels.
Solution Approach 2:
The patent introduces social network relationships as an intermediary layer between buying history and content recommendation. By using social connections and their evaluations as mediators, the system can discover content beyond what the user has directly purchased, thereby expanding recommendations while maintaining relevance through trusted social endorsements.
2Ease of operation
If recommendation is based on another user's preference, then content can be provided, but if the preference differs from the user's actual preference, the user cannot obtain desired content
Solution Approach 1:
The patent implements a feedback mechanism where users evaluate recommended content and provide input that updates the recommender list. This feedback loop allows the system to learn from actual user preferences and adjust recommendations accordingly, improving preference matching accuracy over time while maintaining the ability to provision content based on social recommendations.
Solution Approach 2:
The recommender list is dynamically updated based on user feedback and changing preferences. Rather than relying on static social recommendations, the system adapts the recommender list to reflect current user preferences, ensuring that recommended content remains relevant even as user tastes evolve.
3Measurement precision
If a comprehensive recommender list is maintained with evaluation scores, then accurate recommendations can be provided, but the system complexity increases
Solution Approach 1:
The patent segments the recommender list by content genre, creating specialized recommenders for different types of content. This segmentation allows the system to maintain detailed evaluation scores for each genre without overwhelming complexity, as each genre-specific list can be managed and updated independently with focused data collection strategies.
Data Source
AI summary
There is provided an information processing device including a recommender list obtaining unit obtaining a recommender list in which a recommender of content is associated with an evaluation score of the recommender for on a per-genre basis, a recommended content selecting unit selecting recommended content based on the recommender list, and a sending unit sending the recommended content.