Dynamic Video Catalog Ranking by Contextual Dimensions
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Solution Overview
Problem
Current video-on-demand systems fail to provide personalized and dynamic video content recommendations based on user device type, location, and timeframe, leading to inconsistent search results across different user devices.
Innovation Solution
Implementing a dynamic video content catalog system that integrates usage metrics to rank video assets by popularity within specific dimensions such as device type, location, and timeframe, allowing for personalized search results by filtering and calculating popularity values for each asset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content is presented through a static catalog without personalization, then system complexity is low, but user experience and relevance of recommendations deteriorate
Solution Approach 1:
The patent segments the video catalog into multiple ranked lists based on different dimensions (device type, location, timeframe). Each segment is independently ranked using usage metrics specific to that dimension, allowing personalized recommendations without requiring complete system reconfiguration. This segmentation enables adaptability while managing complexity through modular processing.
Solution Approach 2:
The patent implements dynamic ranking by continuously updating usage metrics and recalculating popularity values based on current time, location, and device type. The catalog transitions from a static structure to a dynamic one where rankings automatically adjust based on real-time usage patterns, enhancing personalization capability while the automated nature of the updates prevents excessive complexity accumulation.
2Adaptability or versatility
If search results are consistent across all devices, then system complexity is low, but relevance to specific user contexts deteriorates
Solution Approach 1:
The patent applies local quality by creating context-specific ranked lists tailored to each combination of device type, location, and timeframe. Instead of applying a uniform ranking system globally, the system generates localized rankings that reflect usage patterns specific to each context, thereby improving relevance while managing complexity through targeted rather than universal processing.
Solution Approach 2:
The patent introduces multiple ranking dimensions (device type, location, timeframe) beyond simple popularity ranking. By adding these dimensional filters, the system creates multi-dimensional ranked lists that provide context-aware recommendations. This dimensional expansion enhances adaptability while the structured approach to multi-dimensional filtering prevents exponential complexity growth.
3Measurement precision
If popularity metrics are calculated for all video assets, then recommendation accuracy is high, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by calculating popularity metrics selectively for video assets within specific ranked lists rather than for all assets universally. The system computes usage metrics and popularity values only for assets relevant to particular device types, locations, and timeframes, thereby maintaining measurement precision for recommended content while reducing overall processing time and computational resource consumption.
Data Source
AI summary
A device receives, from a user device, a search query for video content listings in a video catalog. The device identifies, based on the search query, a set of relevant video assets from an index of the catalog content and determines dimensional values of the search query. The device determines a subset of the relevant video assets based on filtering usage metrics, for the set of relevant video assets, against the dimensional values. The device calculates a popularity value for each video asset in the subset of the relevant video assets and ranks each video asset in the subset of the relevant video assets to form a ranked list. The device sends, to the user device, a response to the search query that includes the ranked list.