Confidence Graphs for Dynamic User Interest Profiling
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Solution Overview
Problem
Conventional techniques for determining user interests rely on incomplete and inaccurate data, such as cookie tracking, surveys, and aggregated financial transactions, leading to erroneous interest graphs that fail to account for dynamic user preferences and authenticity.
Innovation Solution
The use of confidence graphs generated from a user's inbox data, which provides a more authentic and dynamic representation of user interests by analyzing real-time transactional information and evolving user behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques (cookie tracking, surveys, aggregated financial transactions) are used to determine user interests, then data collection is simple and inexpensive, but the data is incomplete and inaccurate leading to erroneous interest graphs
Solution Approach 1:
The patent introduces an email server as an intermediary component that captures and authenticates user interest data through email communications. This intermediary validates user interests by monitoring actual email interactions (opens, clicks, responses) rather than relying on self-reported survey data or cookie tracking, thereby improving measurement precision while maintaining system simplicity
Solution Approach 2:
The system allows users to self-authenticate their interests through their own email communications and interactions. Users provide authentic data about their interests voluntarily through their email behavior (opening emails, clicking links, responding to messages) without requiring complex tracking infrastructure, thus improving data accuracy without proportionally increasing system complexity
2Reliability
If user activity is tracked through cookies and click actions, then implementation is straightforward, but the interest graphs are inaccurate as they represent only initial activity without final transactions
Solution Approach 1:
The system performs preliminary capture of user interest data by monitoring email communications and interactions as they occur in real-time. By capturing interest signals at the email level (which occurs before final transactions), the system establishes an early record of user interests that can be validated and updated throughout the user journey, ensuring completeness without significant time delay
3Measurement precision
If devices are shared among users, then hardware utilization is efficient, but interest graphs become erroneous by mixing data from multiple users
Solution Approach 1:
The patent segments user data by email account rather than by physical device. Each email account maintains separate interest graphs and profiles, allowing multiple users sharing the same device to have completely isolated and accurate user-specific interest data. This segmentation approach preserves measurement precision while accommodating efficient device sharing
4Measurement precision
If ad clicks are tracked, then engagement metrics are collected easily, but the data is misleading due to accidental clicks and clickbait articles
Solution Approach 1:
The email server acts as an intermediary that filters and validates user interest signals by monitoring email opens, clicks, and responses within a controlled email environment. This intermediary layer distinguishes between genuine user interest (evidenced by deliberate email interactions) and spurious signals (accidental clicks or clickbait), improving measurement precision without requiring complex validation algorithms
5Reliability
If survey data is collected from users, then implementation is simple, but responses are inaccurate as users provide aspirational rather than actual interests
Solution Approach 1:
The system replaces self-reported survey data with self-authenticated email behavior data. Users inadvertently provide authentic interest information through their natural email interactions (opening emails from specific senders, clicking links in emails about particular topics, responding to email content), eliminating the need for complex verification of aspirational claims while maintaining system simplicity
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for compiling, updating and dynamically managing a confidence graph for a user that leads to generation of a scored interest profile for the user that content providers can utilize as a basis for disseminating their proprietary digital content. The disclosed confidence graph provides a scored interest profile for each user that is based on authenticated user data derived from an inbox of the user. The confidence graph is not only derived from authenticated data, but is also dynamic and evolves simultaneously with changing user interests. Thus, digital content is selected and transmitted to users based on the current, real-time digital data reflecting their current interests as reflected by their inbox activity.