Group Asset Ranking via Individual Preference Aggregation
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Solution Overview
Problem
Conventional media guidance systems fail to effectively rank assets for groups of viewers with diverse preferences, leading to mismatched content during shared viewing experiences, as they do not consider multiple users' preferences simultaneously.
Innovation Solution
A system that allows users to define groups of viewers with individual preference profiles, computes individual and group preference values using metrics like Euclidean distance, and generates displays showing both individual and group relevance indicators for assets, taking into account priority designations and alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems rank assets based on single user preferences, then the ranking is simple and fast, but it fails to accommodate group viewers with diverse preferences
Solution Approach 1:
The system segments the group preference computation into individual preference evaluations first, then aggregates them. Each viewer's preference is computed separately using Euclidean distance between their profile and asset characteristics, then these individual preferences are combined to form the group preference ranking. This segmentation allows the system to handle group preferences without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary aggregation mechanism that mediates between individual viewer preferences and the final group ranking. The system uses preference aggregation functions (such as averaging or weighted combining) as intermediaries to translate multiple individual preferences into a unified group preference metric, resolving the contradiction between adaptability and complexity.
2Measurement precision
If the system computes detailed individual and group preference values for all assets, then the ranking accuracy improves, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary computation of viewer preference profiles and asset characteristics data structures in advance. By pre-computing and storing these preference vectors and characteristics, the system reduces the computational burden during actual asset ranking operations, maintaining measurement precision while reducing real-time computation time.
Solution Approach 2:
The patent employs parameter changes by using Euclidean distance as a computational metric that can be efficiently calculated. By transforming the preference matching problem into a distance calculation problem in multidimensional space, the system achieves both precision in measurement and efficiency in computation, resolving the time-accuracy tradeoff.
3Loss of information
If the system provides detailed indicators showing individual and group preference values for each asset, then the information completeness improves, but the display complexity increases
Solution Approach 1:
The system applies local quality by providing different levels of information detail in different parts of the display interface. The display shows both individual viewer preferences and aggregated group preferences, but organizes them hierarchically so that users can see the overall group ranking while having access to detailed individual breakdowns when needed. This selective information presentation maintains completeness without overwhelming the interface.
Solution Approach 2:
The patent resolves display complexity by adding another dimension to the information presentation - it displays preference information not just as a single ranking list, but as a multidimensional view showing both individual viewer assessments and group aggregate assessments. This dimensional expansion allows complete information to be presented in an organized, non-overwhelming manner.
Data Source
AI summary
Systems and methods are provided for ranking assets relative to a group of viewers. A group is formed by a user by selecting a number of different viewer preference profiles or automatically by the system as a function of analyzing similarities among viewer preference profiles. Individual preference values are computed for each viewer in the group relative to an asset. A group preference value is then computed by performing a function on the individual preference values. A display is generated that represents the individual preference values and the group preference value relative to an asset. Based on the information provided in the display, the asset most relevant to the group may be selected.