Indirect User Profiling via Sender Metadata Analysis
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Solution Overview
Problem
Existing web portal systems struggle to provide personalized content to users who have not actively specified their preferences, as they rely on user feedback which is not feasible for new users.
Innovation Solution
A system and method for indirectly profiling users based on content items sent by other users, where a profile server monitors and constructs a user profile by assuming delivered links are of interest, using metadata extraction and machine learning techniques to weight user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on user feedback to determine content preferences, then personalization accuracy is improved, but usability deteriorates for new users who cannot provide feedback
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing data from sender users before the target user needs to provide feedback. Sender users pre-select and send content items that are presumed interesting to the target user, creating a head start in building the target user's profile without requiring the target user's active participation.
Solution Approach 2:
The system introduces sender users as intermediaries who mediate the preference collection process. Instead of directly querying the target user for preferences, the system uses sender users who know the target user's interests to indirectly provide preference information through the content items they send.
2Measurement precision
If the system waits for users to actively specify preferences, then profile accuracy is improved, but productivity deteriorates due to delayed personalization
Solution Approach 1:
The system performs preliminary profile construction actions by analyzing sent content items before the target user actively specifies preferences. This allows the system to have personalized content ready much earlier in the user journey, dramatically improving personalization speed while maintaining acceptable accuracy through iterative refinement.
Solution Approach 2:
The system implements a dynamic profile construction process that adapts over time. The profile starts with preliminary data from sender users and continuously evolves as the target user interacts with content and provides feedback, allowing the system to balance initial speed with improving accuracy over time.
3Ease of operation
If the system uses existing user information to build profiles, then ease of operation is improved, but adaptability deteriorates for new users without history
Solution Approach 1:
The system implements a universal profile construction approach that works for both existing and new users. By using sender users as a common data source that can provide information about any target user regardless of their history, the system makes the ease-of-operation benefits accessible to all users universally, not just those with existing profiles.
Solution Approach 2:
The system uses sender users as intermediaries to bridge the gap for new users without history. These intermediaries provide the necessary preference information that would otherwise be unavailable, enabling the system to extend its easy profile creation capabilities to entirely new users who have no existing data in the system.
Data Source
AI summary
A computer-implemented method for constructing a profile for a target user is disclosed. The method comprises monitoring electronic communications across a network to identify at least one electronic communication that identifies a target user as an intended message recipient, extracting metadata from content associated with the at least one electronic communication; and constructing a user profile for the target user on the basis of the extracted metadata. It is assumed that the message senders send their messages, including associated content, to a given target user with the belief that the content is of some interest to the target user on the basis of some knowledge regarding the target user's personality, preferences, tastes and the like. In this manner, a profiling entity may indirectly construct a profile of the target user based on the content sent by one or more message senders to the target user.