Topical Search Result Organization for Query Relevance and Diversity
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Solution Overview
Problem
Existing search systems fail to provide relevant and diverse search results from topical search services due to inadequate query refinement and independent operation, leading to duplicative results and increased computational resource consumption.
Innovation Solution
A dynamic result model generates refined topic queries for multiple topical search services using a transformer architecture, allowing concurrent processing and reducing latency, while minimizing hallucinations and optimizing results for user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the same user query is provided to each topical search service, then the system can invoke multiple search services, but the results become duplicative and less relevant
Solution Approach 1:
The system segments the original user query into multiple refined topic queries, each tailored for specific topical search services. The query segmentation module divides the broad query into specialized sub-queries that better match the expertise of individual search services, reducing duplication and improving relevance.
Solution Approach 2:
The system applies local quality by customizing queries for each topical search service based on its specific domain expertise. Each search service receives a query optimized for its particular topic area, ensuring that the query format and content are locally adapted to maximize the relevance of results from each service.
2Productivity
If multiple topical search services are invoked independently, then the system can gather diverse results, but computational resources are consumed and latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing the user query to identify relevant topics and determine which topical search services should be invoked. This preparation work is done before actual search execution, allowing the system to efficiently coordinate multiple search services and reduce overall latency.
Solution Approach 2:
The system merges the operations of multiple topical search services into a coordinated execution plan. By combining query routing, result aggregation, and ranking into a unified process, the system reduces redundant computational steps and minimizes the time required to gather and process results from multiple services.
3Adaptability or versatility
If generalist large language models are used to provide diversity in search results, then creative content can be generated, but the models are hallucination prone and expensive
Solution Approach 1:
The system introduces an intermediary layer between the user query and the search services. This intermediary query refinement module processes and transforms the original query into specialized topic queries before they are sent to search services, ensuring that the search is guided by structured topic identification rather than relying solely on generative model outputs.
Solution Approach 2:
The system replaces reliance on generalist large language models with a more specialized architecture combining query segmentation, topic modeling, and coordinated search service invocation. This substitution reduces dependence on hallucination-prone generative models while maintaining result diversity through structured query processing and multi-service coordination.
Data Source
AI summary
A method involves responding to a user query by inputting the query and its attributes into a generative model trained to produce multiple topic objects, each pairing a topic query with a topical search service. For each topic object within a selected subset, the method includes obtaining search results by issuing the topic query to the corresponding topical search service. Subsequently, a search result page is provided for the user query, featuring rich result listings that organize the respective search results for the topic objects in the subset. This approach enhances the relevance and depth of search results by leveraging the generative model's ability to associate user queries with specialized topical search services, thereby improving the user's search experience.