Search Relevance Tuning via User Feedback
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Solution Overview
Problem
Current search engines face challenges in delivering relevant results due to the vastness of the internet, ambiguity in user queries, and manipulation through Search Engine Optimization (SEO), requiring a more effective method to determine relevance that incorporates user feedback.
Innovation Solution
A system and method that uses user inputs to tune parameters in a relevance formula, employing machine learning techniques such as statistical classification to rank search results based on user ratings, tags, and feedback, allowing for personalized and community-driven relevance scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines use traditional relevance calculations based on page content and links, then the search process is simple and fast, but the relevance accuracy deteriorates due to SEO manipulation and query ambiguity
Solution Approach 1:
The patent implements feedback mechanisms where user interactions (clicks, dwell time, rankings) are collected and used to retrain the learning-to-rank models. This continuous feedback loop allows the system to adapt to SEO manipulations and improve relevance accuracy over time without requiring complete system redesign.
Solution Approach 2:
The system dynamically adjusts model parameters and weights based on learned patterns from user feedback. By changing the importance weights of different features (content matching, link analysis, user behavior signals) rather than the overall system architecture, the patent improves accuracy while maintaining operational simplicity.
2Measurement precision
If search engines conduct extensive experimentation with test users to adjust relevance methods, then the accuracy improves, but the time consumption and resource requirements increase
Solution Approach 1:
The patent enables the search engine to automatically adjust its own relevance parameters through machine learning models that process real user interaction data. This self-service capability eliminates the need for manual experimentation and test user studies, allowing continuous accuracy improvement without human intervention or time loss.
Solution Approach 2:
The system performs relevance optimization continuously in the background using streaming user feedback data, rather than conducting periodic batch experiments. This continuous learning process ensures the model adapts in real-time to changing search patterns and SEO tactics without disrupting normal search operations or requiring dedicated experimentation time.
3Adaptability or versatility
If search engines use simple relevance formulas, then the system is easy to operate and maintain, but the ability to handle diverse user intentions and personalize results deteriorates
Solution Approach 1:
The patent segments the relevance calculation into multiple independent feature components (query-term matching, document content analysis, link structure evaluation, user behavior signals) that can be separately trained and optimized. This modular approach allows the system to handle diverse user intentions through different feature combinations while maintaining each component's simplicity for easier operation and maintenance.
Solution Approach 2:
The learning-to-rank framework serves multiple functions simultaneously: it personalizes results for individual users, adapts to different query types, counters SEO manipulation, and improves overall relevance. This universal approach handles diverse user intentions through a single flexible system rather than requiring separate mechanisms for each function, balancing adaptability with operational simplicity.
Data Source
AI summary
The present invention is directed to methods of and systems for ranking results returned by a search engine. A method in accordance with the invention comprises determining a formula having variables and parameters, wherein the formula is for computing a relevance score for a document and a search query; and ranking the document based on the relevance score. Preferably, determining the formula comprises tuning the parameters based on user input. Preferably, the parameters are determined using a machine learning technique, such as one that includes a form of statistical classification.