Search Query Relevance Scoring Using Message Hyperlinks
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Solution Overview
Problem
Existing systems struggle to accurately predict the relevance of resources, such as hyperlinks, to search queries, particularly in messaging platforms, leading to irrelevant results.
Innovation Solution
A relevance prediction system utilizing a machine learning model that analyzes previous messages and hyperlinks within a database to determine the relevance of resources based on context, sentiment, and hyperlink types, assigning an overall relevance score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search system returns resources from a database without relevance prediction, then the system is simple and fast, but the relevance accuracy is poor and many irrelevant resources are returned
Solution Approach 1:
A relevance prediction system is introduced as an intermediary component between the search query and the resource database. This mediator uses machine learning models to predict relevance scores for resources before they are returned to the user, filtering out irrelevant results while maintaining system simplicity through automated prediction rather than complex manual filtering mechanisms.
Solution Approach 2:
Manual or rule-based relevance filtering is replaced with an automated machine learning-based prediction system. The machine learning model processes search queries and resource metadata to automatically assign relevance scores, substituting complex manual evaluation mechanisms with an automated intelligent system that improves accuracy without requiring manual intervention.
2Measurement precision
If the search system includes multiple factors for relevance prediction (context, sentiment, hyperlink type), then the relevance accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing resource metadata, context information, and sentiment data in the database before actual search queries are received. When a search query arrives, the relevance prediction system can quickly retrieve and process pre-prepared information rather than analyzing everything from scratch, significantly reducing processing time while maintaining comprehensive relevance assessment.
Solution Approach 2:
The system dynamically adjusts the number and type of factors considered in relevance prediction based on the specific search query and resource characteristics. For simple queries or resources, the system uses fewer factors for faster processing, while complex queries trigger more comprehensive analysis with multiple factors, optimizing the balance between accuracy and processing time through adaptive parameter selection.
Data Source
AI summary
Systems and methods for predicting relevance of resources to search queries. In some aspects, the system may receive a search query requesting resources from a database and may identify resources, including messages, relating to the search query. The system may extract, from the messages, hyperlinks specifying locations within the database. The system may input, into a model, the search query and the hyperlinks to cause the model to generate predictions of relevance of the hyperlinks to the search query. The system may then determine an overall relevance score for the resources in relation to the search query based on the predictions of relevance.