Dynamic ML Model Selection for Web Store Search Ranking
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Solution Overview
Problem
Existing online search systems are limited to using a single trained machine learning model for ranking search results, lacking the flexibility to employ multiple models in parallel, which restricts the ability to provide more relevant search results based on diverse user queries.
Innovation Solution
Implementing a system that stores multiple trained machine learning models, a performance monitoring system, and a decision engine to select the most appropriate model for each search query based on measured performance, ensuring that the best model is used to rank search results for user input queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is used for ranking search results, then the system is simple to manage, but the system lacks flexibility and adaptability to diverse user queries
Solution Approach 1:
The patent segments the search ranking function into multiple independent machine learning models, each trained for specific query types or user behaviors. Instead of one monolithic model, the system divides the ranking task across multiple specialized models that can be selectively applied based on query characteristics, thereby increasing adaptability while maintaining manageable complexity through modular organization
Solution Approach 2:
The patent creates a universal search ranking system that can perform multiple functions by selecting from different machine learning models. The system is designed to handle diverse query types, user demographics, and product categories through a single multi-functional framework that dynamically chooses the appropriate model, achieving versatility without requiring separate dedicated systems for each function
2Reliability
If multiple machine learning models are used in parallel, then the relevance of search results improves, but the system complexity and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple machine learning models offline for different query scenarios, user types, and product categories before deployment. During online operation, the system only needs to select and apply the pre-trained model that best matches the current query, avoiding the complexity of training multiple models in real-time while maintaining high relevance through specialized pre-trained models
Solution Approach 2:
The patent introduces an intermediary component (model selection mechanism) that sits between the query input and the multiple machine learning models. This intermediary analyzes the query characteristics and routes it to the most appropriate pre-trained model, thereby managing the complexity of having multiple models by providing a centralized selection layer that simplifies the decision-making process
3Adaptability or versatility
If a single machine learning model is used, then the computational resources are conserved, but the system cannot adapt to changing user behavior
Solution Approach 1:
The patent performs model selection and adaptation in advance by pre-training multiple models for different user behavior patterns and query types. When a query arrives, the system quickly selects the appropriate pre-trained model rather than training a new model or adapting a single model in real-time, thereby achieving adaptability to changing user behavior while consuming minimal computational resources during online operation
Solution Approach 2:
The patent changes the parameter of model selection dynamically based on query characteristics, user profiles, and contextual information. Instead of using a fixed single model, the system adjusts which model is applied by changing the model selection parameter based on real-time inputs, enabling adaptability to changing user behavior without requiring continuous retraining or complex real-time model modification
Data Source
AI summary
The present technology is directed to high performing trained machine learning models for information retrieval in a web store. In some embodiments, for example, when a search query is received from a user of an online retailer, a computer system associated with the online retailer accesses measurements of performance of sets of search results returned in response to previous user search queries. Each of the previous search results set is a set that was ranked by a machine learning model selected from a store of machine learning models that are each trained to rank search results. Based on the measurements of performance, the computer system selects a machine learning model to rank search results for a response to the received search query. The ranked search results are provided for output to the user.