Actor Recommendation Database Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current media recommendation systems face challenges in optimizing resource usage and ensuring relevance, as they often monitor a vast dataset of actors continuously, leading to high computational costs and potential misses in recommending recently prominent actors, and they lack customization options based on user-specified acting quality.
Innovation Solution
Implement a database update process that differentiates between irrelevant, promising, and relevant actors, with more frequent monitoring of promising actors to ensure timely recommendations and provide content segments matching user-specified acting quality by using threshold scores and review data to categorize actors and select relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the recommendation system continuously monitors information associated with a huge data set of actors, then the system can identify relevant actors timely, but the computational resource requirements increase significantly
Solution Approach 1:
The patent segments the large actor database into multiple smaller databases based on actor scores and prominence levels. This segmentation allows the system to monitor different subsets of actors at different frequencies, reducing overall computational resource requirements while maintaining timely detection of relevant actors through prioritized monitoring of high-value segments.
2Use of energy by moving object
If the recommendation system monitors changes in actor information infrequently, then the computational resource requirements are reduced, but the system may miss out on recommending actors who have recently become prominent
Solution Approach 1:
The patent implements dynamic monitoring frequencies for different actor databases. The system adjusts how often each database is monitored based on the actor scores and prominence levels, with higher-frequency monitoring allocated to databases containing actors more likely to become relevant. This dynamic approach ensures timely detection of prominent actors while optimizing resource utilization.
3Adaptability or versatility
If the system provides all content segments with a selected actor, then the user receives comprehensive information, but the system cannot provide customization based on user-specified acting quality preferences
Solution Approach 1:
The patent applies local quality by implementing acting quality thresholds that filter content segments based on user-specified preferences. Instead of providing all content uniformly, the system applies different quality criteria to different recommendations, allowing customization of acting quality levels while maintaining systematic operation through configurable threshold parameters.
Data Source
AI summary
Systems and methods are disclosed herein for updating, using a specific process that reduces the resource requirements and ensures recommendation relevancy, a particular database that is used for recommending actors. A media guidance application may infrequently search a set of irrelevant actors for an actor who can be classified as a promising actor. The media guidance application may add any promising actor to a set of promising actors. The media guidance application may more frequently search the set of promising actors for an actor who can be classified as a relevant actor. Upon identifying a relevant actor, the media guidance application may include the relevant actor in a set of relevant actors. The media guidance application may then recommend actors to a user based on actors included in the set of relevant actors.