Search Engine Ranking Algorithm Using Class Association Feedback
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Solution Overview
Problem
Current machine learning algorithms for search engines face challenges in accurately ranking documents due to limitations in detecting errors in human-assigned labels and sparse user feedback, leading to suboptimal document relevance in search results.
Innovation Solution
A method and system that utilize a ranking machine learning algorithm to compute amended ranking scores for documents based on their relevance to user queries, incorporating class association values and user feedback, which adjusts the original rank and enhances document relevance, thereby improving user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If human assessors label training documents, then training data is available for ranking models, but labeling requires significant computational and monetary resources and is prone to errors
Solution Approach 1:
The system implements feedback loops where user interactions with search results are collected and used to retrain ranking models. Click-through data and user behavior patterns are fed back into the machine learning algorithm to continuously improve labeling accuracy and reduce reliance on expensive human assessment
Solution Approach 2:
The ranking model performs self-correction by automatically learning from user interaction data without requiring continuous human intervention. The system uses unsupervised learning techniques to identify patterns in user behavior and autonomously adjusts document rankings, reducing the need for manual labeling resources
2Reliability
If ranking models rely on previous user interactions for feedback, then document relevance can be evaluated, but sparse user feedback limits the pool size and variety of training data
Solution Approach 1:
The system processes multiple types of user interaction data (clicks, dwell time, scroll depth, reformulations) through a unified ranking model framework. This multi-functional approach allows the same model to leverage diverse feedback signals, effectively expanding the training data pool from sparse explicit feedback to rich implicit interaction patterns
Solution Approach 2:
The system pre-processes and stores various user interaction metrics in advance, creating a comprehensive feedback repository before ranking decisions are needed. By capturing and structuring user behavior data proactively, the system ensures sufficient training data is available even when individual queries have limited direct feedback
3Productivity
If the ranking model is trained on limited labeled data, then training resources are reduced, but the model's ability to accurately rank documents is compromised
Solution Approach 1:
The system introduces user interaction data as an intermediary signal that bridges the gap between limited labeled training data and accurate ranking performance. This intermediate feedback layer allows the model to learn from abundant unlabeled user behavior patterns, compensating for the scarcity of expert-labeled documents while maintaining high ranking accuracy
Solution Approach 2:
The system dynamically adjusts model parameters and training weights based on the availability and quality of training data. By changing learning rates, regularization parameters, and data sampling strategies according to data scarcity conditions, the model optimizes training efficiency while preserving ranking accuracy even with limited labeled examples
Data Source
AI summary
A system and method of ranking a plurality of documents on a search engine results page (SERP) in response to a query, the method comprising: computing a ranking score for each document, a given ranking score being indicative of an original rank of a respective document; computing a class association value for each document, a given class association value being a probability of a respective document to be associated with a predetermined class of documents having been determined by an operator, a given class association value being instrumental in increasing a magnitude of change of the original rank of the respective document; computing an amended ranking score for each document based on a respective ranking score and a respective class association value; and triggering a display of the SERP with the documents being ranked based on the respective amended ranking score.