Multi-Model Search Ranking via Progressive Complexity Filtering
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Solution Overview
Problem
Conventional online systems face challenges in efficiently ranking search results due to either computational intensity, leading to slow performance, or poor result quality, as they struggle to balance processing all relevant information without excessive resource consumption.
Innovation Solution
The implementation of a multi-model approach that successively applies machine learning models of increasing complexity to analyze and rank search results, leveraging features such as popularity, query, and social features to select and order records effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques process all relevant information to rank search results, then result quality is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the search result ranking process into multiple stages, each handling different types of information with appropriate computational complexity. The system first processes basic relevance features, then progressively incorporates more complex social and contextual features only for promising candidates, rather than analyzing all information for all results.
Solution Approach 2:
The patent applies different levels of analysis depth to different search results based on their initial relevance scores. High-priority results receive comprehensive multi-feature analysis including social features, while lower-priority results receive only basic relevance matching, optimizing resource allocation according to local needs.
2Speed
If conventional techniques use simple ranking methods, then computational speed is improved, but search result quality deteriorates
Solution Approach 1:
The patent implements a dynamic, multi-stage ranking process where the analysis depth and computational methods adapt based on intermediate results. The system dynamically adjusts which features to process and which models to apply, transitioning from fast basic filtering to more computationally intensive social feature analysis only where needed.
Solution Approach 2:
The patent performs preliminary filtering and basic relevance matching before applying more complex ranking models. This preliminary action quickly eliminates obviously irrelevant results, allowing the system to focus computational resources on refining the ranking of promising candidates rather than processing the entire result set with full complexity.
3Loss of time
If online systems return search results quickly without comprehensive processing, then response time is improved, but result relevance deteriorates
Solution Approach 1:
The patent applies partial action by processing only the most relevant features for each search result rather than all available information. The system selectively applies social feature analysis and complex models only to results that pass initial relevance thresholds, achieving good relevance without the overhead of comprehensive processing for every result.
Data Source
AI summary
An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.