Anonymized Content Clustering via Dimensionality Reduction
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Solution Overview
Problem
Personalized content delivery based on individual browsing histories raises security risks and consumes excessive computing resources, and opt-out policies impair content relevance while impeding proper request parsing.
Innovation Solution
The system provides anonymized content retrieval through aggregated browsing history, using a sparse matrix constructed from user data, dimensionally reduced to reduce entropy and maintain anonymity, allowing content selection via quasi-personalized clusters without exposing individual device details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If personalized content delivery is based on individual browsing histories, then content relevance is improved, but security risks increase and computing resources are excessively consumed
Solution Approach 1:
The patent introduces a server as an intermediary that aggregates browsing histories from multiple devices and generates cluster identifiers. Individual devices send their browsing history to the server, which processes the data and returns anonymized cluster identifiers. This intermediary architecture prevents content providers from directly accessing individual browsing histories, reducing security risks while maintaining content relevance through cluster-based personalization.
Solution Approach 2:
The patent merges individual browsing histories into aggregated clusters. By combining data from multiple devices and assigning cluster identifiers, the system transforms individual tracking into group-based anonymization. This merging approach reduces computing resource consumption by processing aggregated data rather than individually analyzing each user's browsing history, while still enabling relevant content delivery.
2Loss of information
If individual browsing histories are tracked for personalization, then content relevance is improved, but computing resources are excessively consumed
Solution Approach 1:
The patent merges individual browsing histories into aggregated clusters to reduce computing resource consumption. By processing aggregated data from multiple devices and generating cluster identifiers, the system avoids the excessive computational burden of individually analyzing each user's complete browsing history, while still enabling relevant content delivery through cluster-based personalization.
Solution Approach 2:
The patent extracts only the essential features from individual browsing histories to create compressed representations. Instead of processing complete browsing histories, the system extracts key patterns and characteristics to generate cluster identifiers, significantly reducing the computational resources required for personalization while maintaining content relevance.
3Adaptability or versatility
If opt-out policies are implemented for privacy, then user control is improved, but content relevance deteriorates and request parsing is impeded
Solution Approach 1:
The server acts as an intermediary that enables privacy protection through aggregation while maintaining content relevance. By processing browsing histories in aggregate and generating cluster identifiers, the system allows users to opt-out of individual tracking while still receiving relevant content based on their cluster's browsing patterns, avoiding the need to impede request parsing.
Data Source
AI summary
The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.