Affinity Score Normalization for Content Creator Engagement
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Solution Overview
Problem
Existing digital content distribution platforms lack a technical solution to effectively quantify user affinity to content creators, as interaction data alone provides limited insight due to complexity from vast volumes and contextual factors.
Innovation Solution
A system and method for computing affinity scores by collecting and normalizing interaction data, using total activity data and attribute data associated with content creators, and generating scores that can be updated periodically, allowing for user account categorization and targeted engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If interaction data is collected and analyzed to determine user affinity to content creators, then user affinity measurement capability is improved, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex affinity determination process into distinct computational modules: collecting interaction data, normalizing the data, computing affinity scores, and determining fan status. This segmentation breaks down the complex task into manageable components that can be processed systematically, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary normalization process that transforms raw interaction data into standardized affinity scores. This intermediary step acts as a mediator between the complex raw data and the simple binary fan status determination, making the data processing more manageable and the final output more interpretable.
2Measurement precision
If comprehensive interaction data is collected to accurately measure affinity, then measurement accuracy is improved, but data volume and processing time increase
Solution Approach 1:
The patent performs preliminary normalization of interaction data before computing affinity scores. By pre-processing and standardizing the data in advance, the system reduces the computational burden during the actual affinity calculation, thereby maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent transforms raw interaction data into normalized affinity scores by changing the parameters and scale of measurement. This parameter transformation converts complex, multi-dimensional interaction data into a standardized metric that is faster to process while preserving the essential affinity information.
3Adaptability or versatility
If affinity scores are computed and updated periodically, then user engagement capability is improved, but computational resources and processing overhead increase
Solution Approach 1:
The patent implements periodic updates of affinity scores rather than continuous computation. By updating scores at scheduled intervals based on new interaction data, the system maintains accurate user engagement metrics while avoiding the continuous computational overhead of real-time processing, thus reducing energy consumption.
Data Source
AI summary
Affinity scores are computed for a plurality of user accounts, each affinity score quantifies an affinity of a user account to a content creator of one or more digital objects hosted in a content hosting platform. Each affinity score is computed by collecting interaction data for the user account corresponding to the content creator, normalizing the collected interaction data using a total activity data of the user account on the content hosting platform and attribute data associated with the content creator, and generating the affinity score using the normalized interaction data.