Key-Value Clusters for Implicit App Query Classification
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Solution Overview
Problem
Current search technologies fail to provide comprehensive results for both implicit and explicit queries for applications, often returning different results regardless of whether the query is implicit or explicit, and do not effectively utilize preliminary search results to enhance search query returns.
Innovation Solution
An automated system and method for generating key-value clusters to classify web results, where a preliminary search result with n web results is used as a key cluster to retrieve and return an associated value cluster, thereby expanding the coverage of search query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current search technology is used to handle implicit and explicit queries, then search queries can be processed, but comprehensive results are not provided and implicit/explicit queries return different results
Solution Approach 1:
The system performs preliminary classification of search queries into implicit and explicit categories before retrieving results. This preliminary action enables the system to apply different processing strategies tailored to each query type, ensuring consistent and comprehensive results across both query types.
Solution Approach 2:
The search result retrieval process is segmented into multiple stages: preliminary search, classification of results, identification of key clusters, and expansion to value clusters. This segmentation allows the system to handle different query types through distinct processing paths while maintaining overall consistency.
2Loss of information
If preliminary search results are used directly, then search processing is simple, but result coverage is insufficient
Solution Approach 1:
Key-value clusters are pre-computed and stored before actual search execution. During search, the system only needs to perform classification and lookup operations on these pre-prepared clusters, dramatically reducing real-time processing complexity while achieving comprehensive result coverage.
Solution Approach 2:
The system creates simplified representations (key clusters) that capture the essential structure of comprehensive search results. These key clusters serve as compact copies that enable fast classification and expansion to full value clusters without processing the entire result set in real-time.
3Loss of information
If key-value cluster expansion is performed, then result coverage increases, but processing time increases
Solution Approach 1:
The system pre-identifies and stores key-value cluster relationships offline. During online search, it only performs classification and retrieval operations on these pre-computed clusters, avoiding the time-consuming task of expanding full result sets in real-time while still delivering comprehensive coverage.
Solution Approach 2:
Instead of retrieving and processing all possible search results, the system performs partial action by focusing only on classifying preliminary results into key clusters and expanding to associated value clusters. This selective approach achieves sufficient coverage without the excessive processing time required for exhaustive result retrieval.
Data Source
AI summary
A system and method of generating and using key-value clusters to classify and return app queries is provided. The system includes a computing device. The computing device includes: a processing device; and a computer readable data storage device storing instructions. When the instructions are executed by the processing device, the instructions are operative to provide: receiving a search query and retrieving a preliminary search result comprising at least n web results; and when the at least n web results matches the key cluster returning the key cluster and an associated value cluster as a result of the search.