Federated Search Result Ranking via Online Click-Through Learning
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Solution Overview
Problem
Enterprise search engines struggle to rank search results from unknown federated sources, requiring users to manually sift through numerous results to determine the best outcomes, as they lack configuration for evaluating results from external sources.
Innovation Solution
Implementing an online learning mechanism to assign weights to search result blocks based on click-through feedback from federated sources, using a click-prediction algorithm to rank and display the most relevant results on a search results page.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the enterprise search engine is not configured for ranking search results from unknown federated sources, then the system maintains simplicity in configuration, but the ability to automatically determine and rank the best results deteriorates
Solution Approach 1:
The search engine performs self-learning by automatically analyzing click-through feedback from users to generate weights for federated sources. The system serves itself by configuring ranking parameters through online learning without requiring manual administrator intervention, thus achieving automatic result ranking while maintaining configuration simplicity.
Solution Approach 2:
The system implements a feedback mechanism where click-through data from users on federated search results is collected and processed. This feedback is used to continuously update and refine the weights assigned to different federated sources, enabling the system to automatically improve its ranking capability over time without additional configuration complexity.
2Device complexity
If users manually peruse through numerous federated search results to determine the best results, then the system maintains simplicity in processing, but the time required to obtain accurate results increases
Solution Approach 1:
The system performs preliminary ranking of federated search results using pre-computed weights before presenting them to users. By pre-processing and ranking results based on historical click-through data, the system reduces the time users need to spend manually evaluating results, while the processing complexity remains manageable through efficient weight-based sorting.
3Measurement precision
If the system assigns weights based on click-through feedback and ranks results automatically, then the accuracy of search outcomes improves, but the complexity of the ranking mechanism increases
Solution Approach 1:
The system improves search result accuracy by dynamically changing the weighting parameters of federated sources based on click-through feedback. Instead of using fixed complexity algorithms, the system adjusts simple weight parameters that reflect source reliability, achieving high measurement precision through parameter optimization rather than complex structural changes.
Data Source
AI summary
Online learning of click-through rates on search result blocks from one or more federated sources may be provided. Click-through feedback for the search result blocks may be received from the one or more federated sources in response to a query. Weights may be assigned to each of the search result blocks based on the received click-through feedback. The search result blocks may then be ranked based on the assigned weights. Finally, a search results page may be generated for displaying the ranked search results blocks to a user.