Adaptive Search Re-ranking via Attractiveness Scoring
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Solution Overview
Problem
Existing search result ranking systems face issues with rank bias, failing to adapt to user trends and not considering factors like revenue and conversions, and are unable to handle new entities or provide a seamless user experience due to neighbor-swapping techniques that require frequent query repetition and jarring visual changes.
Innovation Solution
An adaptive search result re-ranking system that uses a machine-learning model to predict user interaction probabilities and applies time decay to ranking data, allowing for re-ranking based on a weighted combination of clicks, purchases, and conversions, and enabling query clustering for dynamic result optimization without requiring frequent result swapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neighbor-swapping technique is used to counter rank bias, then measurement precision of search result quality is improved, but device complexity and user experience deteriorate due to frequent visual changes and requirement for query repetition
Solution Approach 1:
The system pre-calculates and stores attractiveness scores for search results based on historical interaction data before users perform searches. This preliminary computation of relevance metrics allows the system to present stable, pre-optimized rankings without requiring real-time swapping or repeated queries, thereby maintaining measurement precision while improving user experience.
Solution Approach 2:
Instead of dynamically swapping results which causes visual disruption, the system creates a virtual copy of the search results with adjusted rankings based on attractiveness scores. The original search interface remains stable while the re-ranked copy is presented to users, separating the measurement correction function from the user interface to maintain ease of operation.
2Productivity
If neighbor-swapping technique is used to optimize clicks, then productivity in terms of click optimization is improved, but adaptability to user trends and other metrics deteriorates
Solution Approach 1:
The system generalizes beyond simple click counting by introducing multiple parameters including attractiveness scores, conversion rates, revenue metrics, and time-decay weighted interaction data. These multiple parameters are combined to create a comprehensive relevance score that adapts to various user trends and business objectives, not just click optimization.
Solution Approach 2:
The attractiveness scoring system serves multiple functions simultaneously: it corrects for rank bias, optimizes for clicks, adapts to changing user trends, and can incorporate diverse metrics such as conversions and revenue. This universal approach replaces the specialized neighbor-swapping technique with a multi-functional ranking system.
3Ease of operation
If traditional search ranking is used, then ease of operation is maintained, but measurement precision of result quality deteriorates due to rank bias
Solution Approach 1:
The system introduces an intermediary attractiveness scoring layer between the traditional search ranking and the final result presentation. This intermediary component calculates corrected relevance scores based on historical data and uses them to adjust rankings, thereby maintaining the simplicity of the core search system while improving measurement precision through the intermediary correction layer.
Data Source
AI summary
A system and method to provide adaptively re-ranked search results in response to a search query. An initial ranking of search results associated with a search query cluster including a first search query and a second search query is established. One or more model parameters associated with the first search query are identified. A machine-learning model generates a re-ranking of the search results based at least in part on the initial ranking and the one or more model parameters. In response to receiving the second search query from an end-user system, causing a display of the re-ranking of the search results to the end-user system.