Scored Recommendation System for Multi-Tenant Database Search
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Solution Overview
Problem
Multi-tenant database systems face challenges in providing timely and relevant search results to user queries, as existing systems struggle to efficiently match and rank data objects that are relevant to user inquiries.
Innovation Solution
A method and system for generating scored recommendations in a multi-tenant database system, where data objects of one type are associated with objects of another type, involving the generation of relevance scores and weighted scores to rank and prioritize search results, and transferring these results to user systems via various communication methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system searches and ranks data objects using multiple relevance scoring methods, then the accuracy and relevance of search results is improved, but the processing time and computational complexity increases
Solution Approach 1:
The patent segments the search process into two distinct phases: (1) an indirect search phase that searches case objects first and generates initial relevance scores, and (2) a direct search phase that searches solution objects using those scores as starting points. This segmentation allows the system to leverage the strengths of different search approaches while reducing overall computational burden compared to exhaustive searching of all objects with multiple scoring methods.
Solution Approach 2:
The system performs preliminary actions by first searching case objects to generate initial relevance scores before conducting the main search for solution objects. These pre-computed scores serve as a foundation for the subsequent direct search, eliminating the need to compute all relevance scores from scratch and reducing the time required for the complete search process.
2Reliability
If the system performs comprehensive searching and ranking of data objects, then the quality of search results is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent divides the data objects into two distinct types (case objects and solution objects) and implements type-specific search strategies. Case objects are searched using criteria appropriate for their structure and content, while solution objects are searched using different criteria. This segmentation simplifies the overall system design by allowing specialized handling of each object type rather than requiring a single complex search mechanism that must handle all object types uniformly.
Solution Approach 2:
The patent introduces case objects as intermediary elements that mediate between the user query and the final solution objects. The indirect search through case objects generates relevance scores that guide the direct search for solution objects. This intermediary layer improves search quality by providing contextual filtering and ranking information, while keeping the system manageable by breaking down the complex search task into two simpler, sequential search operations.
Data Source
AI summary
A computer method includes receiving a query from a user system; searching a plurality of data objects of a first type for data objects that substantially match the query; generating a first-relevance score for each data object of the second type that is associated with at least one data objects of the first type identified in the search; searching a plurality of data objects of the second type for data objects of the second type that substantially match the query; generating a second-relevance score for each data object of the second type identified in the second search; generating a list of data objects of the second type that are identified in the first and second search steps; ranking the data objects of the second type in the list of data objects based on the relevance scores; and transferring the list of data objects to the user system.