Application Search System Using Functional Attribute Matching
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Solution Overview
Problem
As the number of available applications grows, users face difficulty in identifying applications that perform specific functions, as existing search technologies rely on keyword matching and fail to effectively identify applications based on desired functionalities.
Innovation Solution
An application search system that generates and updates a search index using application data from various sources, including developer sites, reviews, and social media, to provide a graphical user interface for users to input search queries, and returns relevant applications based on functional attributes, regardless of keyword matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search is used to find applications, then the search process is simple and fast, but the search results do not accurately match user functional needs
Solution Approach 1:
The patent introduces an intermediary layer between the user query and the application database. This intermediary consists of trained machine learning models that translate user functional descriptions into relevant application matches, thereby improving search accuracy without requiring direct complex keyword matching between users and applications.
Solution Approach 2:
The system changes the search parameters from simple keyword text matching to multi-dimensional functional attribute matching. By training models on functional descriptions, usage scenarios, and application characteristics, the search transitions from exact keyword correspondence to semantic functional relevance, significantly improving match accuracy.
2Measurement precision
If comprehensive application data is collected from multiple sources to improve search quality, then the search results become more accurate, but the data processing time and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using comprehensive application data from multiple sources before deployment. This preprocessing step creates ready-to-use models that can perform rapid inference during actual search operations, thereby achieving high search quality without incurring processing delays during user queries.
Solution Approach 2:
The system dynamically adapts to different search scenarios by using trained models that can adjust their predictions based on the specific functional attributes and characteristics of each query. This dynamic approach allows the system to efficiently handle diverse search requirements without requiring manual reconfiguration or extensive real-time processing.
Data Source
AI summary
According to some implementations, a technique for processing an application search query is disclosed. The techniques include receiving a search query from a partner, determining a set of subqueries based on the search query, and extracting query features of the search query from the search query. The techniques further include determining an initial result set of applications based on the set of subqueries and the query features. The initial result set includes a plurality of application representations each defining features of a different application. The techniques also include determining a score for each application in the initial result set of applications based on the set of query features and one or more scoring models, determining a result set based on the scores of the applications in the initial result set, and providing the result set to the partner.