Dynamic Content Assembly for Search Query Relevance
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Solution Overview
Problem
Current search engines face challenges in accurately and efficiently matching third-party content with search results, as existing third-party content may not be directly relevant to specific search queries, leading to user dissatisfaction and increased processing time due to the need for generating numerous content variants.
Innovation Solution
A system that dynamically modifies or generates custom content by applying search query signals, contextual signals, and machine learning models to third-party content, allowing for real-time adaptation and presentation alongside search results, thereby enhancing relevance and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If third-party content is directly used without modification, then processing time is reduced, but relevance to specific search queries deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing third-party content into modular components (headers, descriptions, CTAs) and storing them in a content library. When a search query arrives, the system quickly assembles relevant content variants by combining these pre-prepared components with query-specific signals, avoiding full content generation from scratch while maintaining high relevance.
Solution Approach 2:
The system dynamically adapts third-party content by injecting search query signals, contextual signals, and user-specific signals into content templates. This allows the same base content to be dynamically customized for different queries, maintaining high relevance without requiring separate static content for every possible query scenario.
2Reliability
If numerous content variants are generated to improve relevance, then user satisfaction improves, but processing time and computational resources increase
Solution Approach 1:
The system generates only the necessary portion of content variants by selectively applying signals to specific content components rather than generating complete alternative versions of entire content pieces. The content moderation model ranks and selects only the top-k most relevant variants, avoiding the computational waste of generating and processing excessive numbers of content variants.
Solution Approach 2:
The system segments third-party content into independent modular components (headers, descriptions, CTAs, images) that can be separately processed and recombined. This segmentation allows the system to efficiently generate variants by mixing and matching components rather than processing entire content pieces as monolithic units, significantly improving processing efficiency.
3Reliability
If complex machine learning models are used to generate custom content, then content relevance improves, but computational resources and latency increase
Solution Approach 1:
The system introduces a content moderation model as an intermediary between the content generation process and the final content delivery. This model acts as a filter that quickly ranks and selects the most relevant content variants without requiring complex real-time generation for each query, reducing computational resources while maintaining relevance.
Solution Approach 2:
The system uses lightweight machine learning models that can be efficiently deployed and executed. Instead of relying on extremely complex models, the system employs multiple simpler models (content suitability model, content generation model, content moderation model) that collectively achieve high relevance with lower computational overhead, allowing for efficient copying and deployment across distributed systems.
Data Source
AI summary
Example methods, apparatus, and systems for generating custom content responsive to a received search query are disclosed. An example method for generating custom content responsive to a received search query includes receiving, via a communication interface from a user computing device, a search query including one or more search terms; determining, responsive to the search query, a set of search results relevant to the search query; identifying, responsive to the search query, third-party content and/or a third party relevant to the search query; generating, based on (i) the search query and (ii) the third-party content or the third party, custom content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of research results; and transmitting, via the communication interface to the user computing device, the custom content.