Keyword Phrase Scoring for Contextually Relevant Web Content
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Solution Overview
Problem
Existing systems fail to select contextually relevant content for web pages, often displaying irrelevant or distracting advertisements, leading to low click-through rates.
Innovation Solution
A system that utilizes an indexing engine to analyze user behavioral data and social media content to score keyword phrases, transforming them into user-selectable links that trigger the display of contextually relevant bundled content, including ads and social media items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing content selection systems are used to select advertisements for web pages, then content can be displayed on web pages, but the selected content is often irrelevant or distracting to users
Solution Approach 1:
The patent segments the content selection process into multiple independent analysis components: (1) extracting key phrases from the web page, (2) analyzing user behavioral data separately, (3) scoring each key phrase independently based on multiple criteria including user behavior, and (4) selecting content based on aggregated scores. This segmentation allows each component to be optimized independently, improving overall content relevance while filtering out distracting content through multi-criteria evaluation.
Solution Approach 2:
The patent changes the parameters used for content selection from simple keyword matching to a multi-dimensional scoring system that incorporates user behavioral data (click-through rates, time on page, bounce rates), content quality metrics, and contextual relevance scores. By transforming the selection criterion from a single parameter to multiple weighted parameters, the system achieves better adaptability to user preferences and reduces harmful distractions.
2Productivity
If existing advertisement selection systems are used, then advertisements can be displayed on web pages, but the click through rate remains low (less than 1%)
Solution Approach 1:
The patent implements a feedback loop where user behavioral data (clicks, impressions, time spent, bounce rates) is continuously collected and used to update the scoring of key phrases and content suitability. This feedback mechanism allows the system to learn from user interactions and continuously improve content selection, thereby increasing click-through rates while maintaining reliability through data-driven decisions rather than random or rule-based selection.
Solution Approach 2:
The patent performs preliminary analysis of user behavioral data and key phrase scoring before content selection, creating a pre-computed relevance profile for each web page. This preliminary action includes extracting key phrases, scoring them based on historical user behavior, and pre-ranking potential content options, so that when content needs to be displayed, the selection can be made quickly from pre-evaluated options, improving both click-through rate and content suitability reliability.
Data Source
AI summary
A process is described for assessing the suitability of particular keyword phrases for use in serving contextually relevant content for display on pages of network-accessible sites. In one embodiment, the process involves scoring the key phrases based in part on collected user behavioral data, such as view counts of associated social media content items. A process is also disclosed in which selected keyword phrases on a page are transformed into links that can be selected by a user to view bundled content that is related to such keyword phrases.