Taste Profile Attributes for Media Data Summarization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The sheer volume of user data from media consumption creates challenges for service providers to understand user preferences and apply that understanding to enhance the listener's experience, as existing systems struggle to distill meaningful insights from vast amounts of music, video, game, and book usage data.
Innovation Solution
The development of systems, methods, and computer program products that generate and utilize taste profiles by calculating statistics such as familiarity, trending popularity, and stylistic diversity, allowing for personalized recommendations and improved user experience through the analysis of media content activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If systems collect and store vast amounts of user media consumption data, then the quantity of available data increases, but the difficulty of extracting meaningful insights from this data increases
Solution Approach 1:
The patent extracts meaningful taste profile attributes from the vast volume of user media consumption data by identifying and isolating key statistical patterns (familiarity, trending popularity, stylistic diversity). This extraction process transforms raw data into actionable insights that represent user preferences without requiring processing of every individual data point.
Solution Approach 2:
The system transforms complex media consumption data into simplified taste profile attributes by changing the parameters of representation. Instead of storing raw consumption records, the system computes and stores aggregated statistical parameters (mean, variance, skewness) that capture essential user taste characteristics in a compact form.
2Loss of information
If systems process and analyze vast amounts of user media consumption data, then the amount of information available increases, but the computational complexity and resources required increase
Solution Approach 1:
The patent segments the complex task of analyzing media consumption data into distinct computational steps: collecting consumption data, computing statistical parameters (mean, variance, skewness) for each media item, aggregating these parameters into taste profiles, and generating recommendations. This segmentation allows each step to be optimized independently and reduces overall computational burden.
Solution Approach 2:
The system performs preliminary computation of statistical parameters and taste profile generation during data collection and processing phases, so that when recommendations are needed, the foundational analysis is already complete. This preliminary action reduces the computational burden during the actual recommendation generation process.
3Measurement precision
If systems store detailed user media consumption history, then the accuracy of user preference analysis increases, but the data storage requirements increase
Solution Approach 1:
The patent transforms detailed consumption history into compact taste profile attributes by changing the data representation from raw transactional records to aggregated statistical parameters. This parameter transformation maintains the essential information needed for accurate preference analysis while dramatically reducing storage requirements.
Solution Approach 2:
The system extracts only the essential statistical characteristics (mean, variance, skewness) from detailed consumption histories and stores these condensed representations as taste profiles. This extraction retains the core information needed for accurate user preference analysis while eliminating redundant detailed data from storage.
Data Source
AI summary
Methods, systems and computer program products are provided for summarizing user activity associated with media content by accessing a taste profile containing a representation of media content activity corresponding to at least one of a plurality of items, generating at least one statistic corresponding to the media content activity, and generating a taste profile attribute by using the at least one statistic.