Search Ranker Training via Automatic Relevance Inference
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Solution Overview
Problem
Search engines often fail to accurately rank relevant items based on a user's level of interest, as existing approaches rely on expensive and difficult-to-acquire manually-labeled training samples, making it challenging to keep search results up-to-date with constantly changing web data.
Innovation Solution
A search result ranker is trained using automatically-generated samples from user interactions, such as dwell time and click-through rates, to infer relevance scores and formulate query-identifier-relevance score triplets, which are then used to train a learning machine, eliminating the need for manual labeling and enabling continuous data updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually-labeled training samples are used to train search result rankers, then ranking accuracy improves, but acquisition cost and difficulty increase significantly
Solution Approach 1:
The system uses user interactions with search results (clicks, dwell time, re-rankings) to automatically generate training labels without manual intervention. Users effectively label their own search behavior data, enabling continuous automatic generation of training samples that reflect actual user preferences and interests.
Solution Approach 2:
Instead of manually creating training samples, the system copies and utilizes existing user interaction data from production search systems. This copying approach transforms operational search log data into training samples, eliminating the need for separate manual labeling processes while maintaining relevance to actual search scenarios.
2Adaptability or versatility
If search engines continuously update with changing web data, then relevance to user interests improves, but training data acquisition complexity increases
Solution Approach 1:
The system continuously collects user interaction data from ongoing search operations and continuously retrains rankers without interruption to search services. This continuous process ensures the ranking model adapts to changing user interests and web content while maintaining operational search functionality throughout the training process.
Solution Approach 2:
The same search system that serves users also generates training data and performs model training. The search infrastructure is multi-functional, handling both production queries and training data generation, eliminating the need for separate dedicated training systems and reducing overall complexity.
3Reliability
If large volumes of training samples are generated, then model performance improves, but processing time and computational resources increase
Solution Approach 1:
The system uses incremental training approaches where models are updated with subsets of training data over multiple iterations rather than processing all data at once. This allows continuous improvement of model performance using progressively larger portions of available training samples without requiring complete retraining each time.
Data Source
AI summary
A search result ranker may be trained with automatically-generated samples. In an example embodiment, user interests are inferred from user interactions with search results for a particular query so as to determine respective relevance scores associated with respective query-identifier pairs of the search results. Query-identifier-relevance score triplets are formulated from the respective relevance scores associated with the respective query-identifier pairs. The query-identifier-relevance score triplets are submitted as training samples to a search result ranker. The search result ranker is trained as a learning machine with multiple training samples of the query-identifier-relevance score triplets.