Query Clustering for Search Result Ranking
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Solution Overview
Problem
Traditional search result ranking systems struggle to understand the nuances of natural language and user intent, leading to suboptimal retrieval performance, despite advancements in pre-trained language models like BERT and ROBERTa.
Innovation Solution
The proposed system employs query clustering to refine search result ranking by training baseline and query-specific models, using embeddings vectors to group similar queries and adapt models to different query types, thereby improving retrieval performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pre-trained language models (BERT, ROBERTa) are used for search result ranking, then the system can process natural language queries, but the retrieval performance remains suboptimal due to inability to capture nuanced user intent
Solution Approach 1:
The patent segments the single pre-trained language model into multiple specialized models, each trained on specific query clusters (e.g., navigation queries, transactional queries, informational queries). This segmentation allows each model to specialize in capturing particular types of user intent, thereby improving both retrieval performance and adaptability to different query nuances.
Solution Approach 2:
The patent applies local quality by training different models with different characteristics for different query types. Each query cluster receives a customized model trained on its specific characteristics, allowing the system to apply locally optimized retrieval strategies rather than a one-size-fits-all approach, thus improving understanding of nuanced user intent.
2Adaptability or versatility
If a single pre-trained language model is used for all queries, then the system structure remains simple, but the model cannot adapt to different query types and user intents
Solution Approach 1:
The patent applies preliminary action by pre-clustering queries into distinct categories before training specialized models for each cluster. This preliminary organization of query types enables the system to efficiently route incoming queries to the most appropriate specialized model, achieving adaptability to different query types while maintaining manageable system complexity through structured preprocessing.
Solution Approach 2:
The patent changes key parameters of the modeling approach by training multiple models with different hyperparameters, architectures, or training data configurations tailored to specific query clusters. This parameter variation allows each model to be optimized for its intended query type, improving adaptability while the modular structure keeps overall complexity可控.
3Measurement precision
If query clustering is implemented to improve retrieval performance, then more fine-grained representations of user intent are achieved, but the system complexity increases due to multiple models
Solution Approach 1:
The patent extracts and isolates specific query clusters from the general query space, creating dedicated models only for the most critical or distinct query types. By taking out and separately handling only the query clusters that benefit most from specialization, the system achieves improved retrieval performance for those specific cases while limiting the increase in overall system complexity.
Solution Approach 2:
The patent implements a universal query routing mechanism that can direct different query types to appropriate specialized models. This multi-functional routing system serves as a central coordinator that manages multiple specialized models through a single unified interface, thereby hiding the complexity of having multiple models from the user and simplifying the overall system interaction.
Data Source
AI summary
A method for providing a ranked set of search results includes receiving, from a device, a query, determining, via a first machine learning model, an initial set of documents responsive to the received query, accessing a second machine learning model, the second machine learning model selected from a set of machine learning models based on a characteristic of the query, ranking, via the second machine learning model, the initial set of documents based on responsiveness to the query to generate the ranked set of search results, and outputting the ranked set of search results on the device.