Weighted Content Ranking for Network Strain Reduction
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Solution Overview
Problem
Existing content recommendation systems in communication networks often provide inaccurate recommendations, leading to bandwidth and performance strain due to inefficient content caching, as they rely solely on total daily views without considering varying price points and availability windows, which can misrepresent a content item's popularity.
Innovation Solution
A recommendation ranking system that uses weighted values based on accessibility and availability periods to adjust viewing and browsing data, incorporating pricing variations and user characteristics to generate personalized rankings, thereby improving the accuracy of content recommendations and optimizing cache storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content recommendations are based on total daily views without considering price points and availability windows, then the recommendation system is simple to implement, but the recommendation accuracy deteriorates
Solution Approach 1:
The patent transforms the single metric of total daily views into multiple weighted parameters including price-sensitive views, availability window views, and user characteristic-based views. Each parameter is assigned a weight reflecting its importance, and the recommendation accuracy is improved by aggregating these weighted parameters rather than relying on a single crude metric.
Solution Approach 2:
The patent segments the overall viewing data into distinct categories based on price sensitivity, availability windows, and user characteristics. By dividing the monolithic view count into these segments and analyzing each separately with appropriate weighting, the system achieves more nuanced and accurate recommendations without being overwhelmed by complexity.
2Productivity
If content items are cached based on inaccurate recommendations, then cache storage is simplified, but network bandwidth and performance strain increase
Solution Approach 1:
The patent implements a feedback mechanism where actual user viewing behavior and price sensitivity data are continuously collected and used to refine the recommendation algorithm. This feedback loop ensures that content caching decisions are based on increasingly accurate predictions of what users will actually watch, reducing unnecessary bandwidth consumption for unpopular content.
Solution Approach 2:
The system dynamically adjusts caching strategies by changing the parameters used to evaluate content popularity. Instead of using static view counts, the system modifies the evaluation parameters to include price sensitivity weights and availability window factors, ensuring that cached content is more likely to be actually viewed, thereby improving network efficiency.
3Ease of operation
If content recommendations do not account for price-sensitive user behavior, then the recommendation algorithm is simpler, but user satisfaction deteriorates
Solution Approach 1:
The patent applies local quality by tailoring the recommendation algorithm to different user segments based on their price sensitivity and viewing preferences. Instead of a one-size-fits-all approach, the system adjusts the weighting parameters locally for different user groups, providing personalized recommendations that resonate with each segment's specific characteristics and price sensitivity.
Solution Approach 2:
The recommendation algorithm is made dynamic by continuously adapting the weights assigned to different parameters based on changing user behavior, price points, and availability windows. This dynamic adjustment allows the system to respond to evolving user preferences and market conditions, maintaining high user satisfaction without requiring complete algorithm redesign.
Data Source
AI summary
Methods, systems, and apparatuses are described for a recommendation ranking system that uses viewing and/or browsing data of content items available from a communication system received from users of the system. The viewing and browsing data may be obtained from the users through different availability periods having varying price points for viewing the content items. Weighted values may be applied to the viewing and browsing data based on the price point or availability period of the content items associated with the collected data. A popularity index for the content items of the communication system may be determined based on the adjusted viewing and browsing data and a ranking of recommendations for content items may be generated based at least on the popularity index of the content items.