Search Ranking System Predicting Content Usefulness
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Solution Overview
Problem
Existing ranking systems for search engine results pages (SERPs) struggle to effectively rank content elements, such as images and videos, due to their unique size and appearance, which differ from traditional elements, leading to suboptimal user interaction and relevance in search results.
Innovation Solution
A method and system that train a ranking system to predict usefulness scores for elements based on their size and distance from the top of the SERP, using user interaction data to determine optimal placement, allowing for improved ranking of content elements by considering their size and position within the SERP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ranking methods are used for content elements, then the ranking system is simple to implement, but the relevance and user interaction with search results deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the ranking system using historical user interaction data before actual search operations. The system performs offline training to learn optimal ranking factors for content elements, then applies these pre-learned rankings during live operations. This resolves the contradiction by preparing the system in advance, improving relevance without adding real-time complexity.
Solution Approach 2:
The patent implements feedback mechanisms by continuously collecting user interaction data (clicks, views, engagement time) and using this feedback to iteratively improve the ranking model. The system monitors actual user behavior with content elements and adjusts ranking algorithms based on this feedback, resolving the contradiction between simplicity and effectiveness through data-driven optimization.
2Productivity
If content elements are placed at higher positions in SERP, then user interaction increases, but the distance from top increases which may reduce effectiveness
Solution Approach 1:
The patent applies local quality by treating different positions in the SERP differently based on their characteristics. Instead of uniform ranking treatment, the system optimizes content element placement specifically for their optimal positions, considering that content elements may perform better at certain distances from the top compared to traditional search results. This resolves the contradiction by making positioning strategies position-specific rather than uniform.
3Measurement precision
If the ranking system considers multiple factors including size and position, then the accuracy of usefulness prediction improves, but the computational complexity increases
Solution Approach 1:
The patent resolves the computational complexity issue by performing complex multi-factor analysis during offline training phases rather than real-time operations. The system pre-computes the relationships between element characteristics (size, position, type) and user interaction outcomes, storing these as learned models. During actual search operations, the system applies these pre-computed models efficiently, achieving high prediction accuracy without real-time computational burden.
Data Source
AI summary
A method and system for determining rank positions of elements displayed on a search engine results page (SERP) by a ranking system. During a training phase of the ranking system, an indication of user interactions associated with a first ranked list of elements is acquired. The elements of the first ranked list were visually displayed on a first SERP. A score for a first content element is determined based on a size of the first content element and a ranking of the first content element. The ranking system is trained, based on the score, to predict a predicted usefulness score for the first content element. During an in-use phase of the ranking system, a ranked list of elements is determined based on a query. The ranking system determines a ranked position for a content element in the ranked list. A SERP based on the ranked list is output.