Multi-Source Content Recommendation by Bandwidth, Resolution, and QoE
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Solution Overview
Problem
Conventional content discovery applications struggle to recommend the highest-quality version of a content item available from multiple OTT providers due to factors like network quality, device capability, and user experience, leading to user uncertainty in selection.
Innovation Solution
A system that determines the quality of content items from multiple sources based on network bandwidth, device resolution, and user experience (QoE) to rank and recommend the highest-quality content item for playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional content discovery applications list all available content items from multiple OTT providers, then the user has access to complete information about content availability, but the user becomes uncertain of which content item to select due to lack of quality differentiation
Solution Approach 1:
The patent introduces a recommendation system as an intermediary between the content discovery application and the user. This intermediary processes multiple factors (network bandwidth, device resolution, QoE metrics) to generate a recommendation factor that ranks content items, thereby mediating the information gap between raw content availability data and user decision-making needs.
Solution Approach 2:
The system transforms multiple quality parameters (bandwidth, resolution, QoE) into a single composite recommendation factor that ranks content items. This parameter transformation consolidates complex quality assessments into an actionable ranking system that directly guides user selection, converting abstract quality metrics into a clear selection hierarchy.
2Manufacturing precision
If the system recommends the highest-quality content item based on multiple factors, then the user receives optimal playback quality, but the system complexity increases due to multiple evaluation criteria
Solution Approach 1:
The recommendation system is segmented into distinct functional modules: a network quality assessment module that evaluates bandwidth, a device capability module that assesses resolution, a QoE measurement module that tracks user experience metrics, and a recommendation factor calculation module that synthesizes these inputs. This segmentation allows each module to handle specific evaluation tasks independently, reducing overall system complexity while maintaining comprehensive quality assessment.
3Reliability
If the system considers network bandwidth, device resolution, and QoE to determine content quality, then the recommended content item ensures optimal playback, but the determination process requires extensive data collection and processing
Solution Approach 1:
The system performs preliminary assessments of network bandwidth, device resolution, and QoE metrics before the user actually selects content. By pre-calculating these parameters and maintaining updated profiles of device capabilities and network conditions, the system prepares the foundation for rapid recommendation generation, reducing the time required at the moment of content selection while ensuring reliable quality assessment.
Data Source
AI summary
Systems and methods are described for recommending a content item. A search query for a content item is received. The availability of the content item from more than one source is determined. In response to determining that the content item is available from more than one source, the quality of each of the available content items from respective sources is determined. A recommendation factor is determined. The recommendation factor is based on at least one of the bandwidth available to a user device, the resolution capability of the user device, and the quality of experience of each of the sources from which the content item is available. A list of search results for the available content items is generated. The list is ordered based on the quality of each of the available content items from respective sources and the recommendation factor.