Performance Recommendation Module for Excitement-Based Content Filtering
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Solution Overview
Problem
Sports fans face challenges in identifying exciting sports performances among numerous options, as existing services often spoil the excitement by revealing scores or highlights, leading to inefficient use of time and potential loss of viewership.
Innovation Solution
A method and system that recommend live and recorded performances based on their excitement level, using a recommendation module to score discrete events within the performances, considering subscriber preferences to preserve the suspense and excitement, and providing personalized viewing times to maximize engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing services provide game highlights and box scores, then information availability is improved, but excitement preservation deteriorates
Solution Approach 1:
The system performs preliminary analysis of performance data to identify exciting events and generates recommendations before the user views the content. By pre-processing the performance data to detect key moments and calculate excitement metrics, the system can guide users to watch only the most exciting segments without revealing outcomes in advance, thus preserving excitement while providing information.
2Adaptability or versatility
If all available performances are broadcast to fans, then content variety is improved, but time efficiency deteriorates
Solution Approach 1:
The system extracts and isolates only the most exciting segments from complete performances by analyzing performance data for key events, score changes, and unusual occurrences. Instead of presenting entire games or performances, the system extracts and recommends specific highlight segments based on calculated excitement metrics, allowing users to consume diverse content efficiently without watching unnecessary portions.
Solution Approach 2:
The system automatically analyzes performance data, identifies exciting events, and generates personalized recommendations without requiring user intervention. By implementing self-service through automated excitement detection and recommendation generation, the system filters content based on objective metrics rather than relying on users to manually browse and select from all available performances.
3Productivity
If personalized recommendations are implemented, then user engagement is improved, but system complexity deteriorates
Solution Approach 1:
The system personalizes recommendations by dynamically changing the parameters used to calculate excitement metrics based on individual user preferences and viewing history. Instead of complex adaptive algorithms, the system adjusts weighted factors in the excitement calculation formula to reflect user-specific preferences, achieving personalization through parameter modification rather than structural complexity.
Data Source
AI summary
A method and system to recommend performances based on the performances' excitement-level as determined from events and characteristics associated with the performance and a subscriber's preference is described herein. A subscriber's preference influences the performance type and performance characteristics that are exciting to the subscriber. Recommendations for performances can be sent to the subscriber as a link or direct connection to a location of the online performance. Recommendations of a performance preserve the excitement of the performance by not revealing the score, outcome, or any statistics or commentary that would spoil the natural buildup of excitement from watching the live performance. Performance recommendations may include a start-time and end-time for watching only the exciting portions of the performance. Recommended portions of performances can be ranked to fit within a subscriber's customizable viewing-time, so that a subscriber can view only the most exciting portions of performances that fit within a limited time-frame.