Network Content Reputation Scoring via User Behavior Feedback
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Solution Overview
Problem
Conventional methods for indexing network content fail to accurately identify undesirable content, as they are often content-neutral and do not effectively reflect user interest, leading to difficulties in distinguishing desirable from undesirable content.
Innovation Solution
A system that determines the interest level of network content based on actual user behavior, such as content request traffic patterns, to identify both positive and negative content, using techniques like rate-of-change analysis and gain functions to calculate interest weights for content sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search engines index web pages according to page contents and metadata, then content accessibility is improved, but measurement precision of content nature is worsened
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with content (such as time spent on page, clicks, downloads) are collected and used to update the reputation score of content sources. This feedback loop allows the system to continuously improve its ability to identify desirable versus undesirable content based on actual user behavior patterns rather than relying solely on static indexing metadata.
Solution Approach 2:
The patent replaces the mechanical system of manual content classification (like library classification systems) with an automated electronic system that uses algorithms to analyze user behavior data. This substitution enables dynamic, real-time assessment of content quality without human intervention, significantly improving measurement precision while maintaining ease of access.
2Adaptability or versatility
If no standard for organizing web-based content exists, then adaptability of content publishing is improved, but difficulty of detecting and measuring content quality is worsened
Solution Approach 1:
The patent introduces new parameters for content assessment, specifically reputation scores derived from user behavior metrics such as time spent on content, number of clicks, download frequency, and user ratings. These parameters provide a quantitative basis for evaluating content quality without imposing rigid organizational standards, thus maintaining publishing flexibility while enabling systematic quality measurement.
Solution Approach 2:
The patent introduces an intermediary layer between content publishers and users in the form of a reputation scoring system. This intermediary automatically analyzes user interactions and provides quality indicators to both publishers and users, facilitating content discovery and quality assessment without requiring standardized content organization or manual review processes.
3Measurement precision
If user behavior data is collected to determine content interest, then measurement precision of content relevance is improved, but loss of information privacy is worsened
Solution Approach 1:
The patent extracts only the necessary behavioral data elements required for reputation scoring (such as time spent on page, click patterns, and download actions) without collecting comprehensive user information. By taking out only the specific data points needed for content quality assessment, the system achieves measurement precision while minimizing privacy intrusion.
Solution Approach 2:
The system enables users to implicitly contribute to content quality assessment through their natural browsing and interaction behaviors without requiring explicit data submission or consent processes. Users essentially self-report their content preferences and experiences through their actions, reducing the need for active privacy-sensitive data collection while still gathering valuable behavioral insights.
Data Source
AI summary
A method and system for determining and notifying users of undesirable network content are disclosed. According to one embodiment, a method may include detecting an adverse content event corresponding to a given network information source, where the adverse content event occurs dependent upon activity of a given user with respect to the given network information source. The method may also include reporting the adverse content event with respect to the given network information source, detecting a reference to the given network information source on behalf of a particular user, and in response to detecting the reference, retrieving an indication corresponding to the given network information source, where the indication is determined dependent upon adverse content events reported with respect to the given network information source. The method may further include notifying the particular user of possible undesirable content with respect to the given network information source dependent upon the indication.