Content Feature Recommendation via Audience Segmentation and Advertiser Weights
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Solution Overview
Problem
Content producers face challenges in creating targeted content for diverse and segmented audiences, as traditional television advertising struggles to adapt to the data-driven models of internet advertising, necessitating improved techniques for content planning based on audience rating data analysis and advertiser interests.
Innovation Solution
A method that combines historical audience data with advertiser investment interests to recommend content features by calculating pairwise similarity between content preferences and advertiser weights, generating a ranked list of existing content items and summarizing features for future content production, using data mining and recommendation systems to identify top-performing content characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional television advertising models are used to reach large audiences, then broad audience coverage is achieved, but targeting precision and data-driven customization deteriorate
Solution Approach 1:
The patent segments the audience into distinct demographic and behavioral groups, analyzing historical rating data to identify specific audience segments with shared characteristics. This allows content to be tailored to each segment while maintaining broad overall coverage through multi-segment targeting strategies.
Solution Approach 2:
The system changes the parameters of content planning by incorporating advertiser-specific weight vectors for different audience segments. By adjusting the importance weights of various audience segments based on advertiser preferences, the system optimizes content features to simultaneously appeal to multiple segments with different characteristics.
2Reliability
If data-driven content planning techniques are implemented to improve targeting, then advertising ROI improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary analysis of historical audience rating data and advertiser preferences before content production begins. By pre-calculating audience segment characteristics, content feature importance weights, and segment similarities in advance, the system reduces real-time processing complexity while maintaining high targeting accuracy.
Solution Approach 2:
The system introduces intermediary computational structures including weight vectors for audience segments, similarity metrics between segments, and ranked lists of content features. These intermediaries bridge the gap between raw data and content decisions, simplifying the overall system architecture by breaking down complex multi-objective optimization into manageable sequential steps.
3Measurement precision
If content is customized for specific audience segments, then targeting effectiveness improves, but production costs and complexity increase
Solution Approach 1:
The patent identifies content features that serve multiple audience segments simultaneously by analyzing feature importance across different segments. Content produced with these universal features can appeal to multiple segments without requiring separate customizations, reducing production complexity while maintaining targeting effectiveness through multi-segment relevance.
Data Source
AI summary
Content planning techniques are provided that recommend content features based on the investment interest of advertisers in various audience segments and historical audience measurements. An exemplary method comprises obtaining historical data comprising content preferences indicating a performance metric for each pair of a plurality of content items and audience segment, wherein the content items comprise a plurality of content features indicating characteristics of a corresponding content item; obtaining, for each of a plurality of advertisers, a weight indicating a future interest of a given advertiser in a given audience segment; calculating a pairwise similarity between a vector of the content preferences and a vector of the weights for the plurality of the audience segments to obtain a ranked list of the content items sorted by the pairwise similarity; and generating a summarization of the content features to be used in future content items based on the ranked list.