Automated Content Position Recommendations
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Solution Overview
Problem
In conventional web publishing, human editors inefficiently decide the placement of content elements on web pages, lacking an automated approach to determine optimal positions for multiple content links, which affects user engagement.
Innovation Solution
A system and method that collects user activity data to calculate engagement metrics for content links, predicting optimized positions and generating recommendations for their placement on web pages, utilizing a content position recommendation module that includes user data collection, web page layout identification, and position recommendation sub-modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human editors manually decide the placement of content elements on web pages, then content positioning can be customized based on editorial judgment, but the process is inefficient and time-consuming
Solution Approach 1:
The system enables automated content link positioning by using machine learning models to independently determine optimal positions without human editor intervention. The content position recommendation module automatically analyzes user activity data and generates positioning recommendations, allowing the system to serve itself rather than relying on manual editorial decisions.
Solution Approach 2:
The patent replaces the mechanical manual process of editors physically placing content elements with an automated computational system. The content position recommendation module uses algorithms and machine learning to substitute the manual mechanical action of dragging and dropping content, thereby improving efficiency and reducing time loss.
2Productivity
If automated systems are used to determine content link positions, then placement efficiency improves, but the system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a content position recommendation module that generates positioning suggestions, a user activity data collection module that gathers interaction data, and a machine learning module that processes the data. This segmentation allows each component to be developed and maintained independently, managing overall system complexity while achieving automated content placement.
Solution Approach 2:
The content position recommendation module acts as an intermediary between user activity data and final content placement decisions. It processes raw user interaction data through machine learning algorithms to generate optimized positioning recommendations, serving as a mediator that translates complex data into actionable placement suggestions without requiring direct complex interactions between all system components.
3Productivity
If content links are placed without optimization, then the web page can be published quickly, but user engagement and content consumption decrease
Solution Approach 1:
The system implements feedback loops by continuously collecting user activity data from web page interactions and using this feedback to refine content positioning recommendations. The machine learning model learns from user behavior patterns and adjusts positioning strategies accordingly, creating a closed-loop system that improves user engagement through data-driven optimization while managing complexity through iterative learning.
Data Source
AI summary
A user data-driven method and system for generating position recommendations for content links on a web page is disclosed. The method and system collect user activity data associated with a set of content links positioned in one or more content position boxes of the multiple content position boxes of a web page during an activity window. An engagement measurement associated with a particular content link of the set of content links during the activity window is determined. A performance delta for a particular content link based on the calculated engagement measurement associated with the particular content link is determined. Multiple predicted user engagement measurements associated with moving the particular content link to a plurality of updated positions is determined. The method and system generate content position recommendations associated with the particular content link based on a comparison of the multiple predicted user engagement measurements.