Database Search Ranking via User Behavior Weighting
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Solution Overview
Problem
Current database search systems fail to effectively rank search results based on user preferences and recent behavior, often presenting irrelevant information due to outdated access history and lack of understanding of field significance.
Innovation Solution
A computer system that analyzes previous database access records to calculate field and field value weights, using a machine learning model to rank search results based on user affinities, without requiring understanding of field meanings, and adjusts rankings dynamically based on recent access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If database search results are ranked using traditional methods without user behavior analysis, then the search system is simple and fast, but the relevance of search results to user needs deteriorates
Solution Approach 1:
The system performs preliminary analysis of user access patterns and calculates field weights before the actual search operation. By pre-processing user behavior data and establishing weighting criteria in advance, the system prepares the ranking framework beforehand, enabling relevant results to be presented without adding significant complexity to the search execution itself.
Solution Approach 2:
The system automatically analyzes user access patterns and generates field weights without requiring manual configuration or intervention. The machine learning model self-adjusts the ranking criteria based on observed user behavior, enabling the system to improve search relevance autonomously while maintaining operational simplicity.
2Adaptability or versatility
If the system uses outdated access history for ranking, then the system is stable and consistent, but the adaptability to user current needs deteriorates
Solution Approach 1:
The system dynamically adjusts field weights based on the temporal characteristics of user access patterns. By analyzing recency and frequency of accesses, the model adapts weighting criteria to reflect current user needs while maintaining stability through consistent application of the temporal analysis framework. This enables the system to balance adaptability with ranking consistency.
Solution Approach 2:
The system periodically updates field weights based on accumulated user behavior data, rather than using a static or purely real-time approach. This periodic re calibration allows the system to maintain stability during intervals while adapting to changing user needs over time, achieving a balance between consistency and adaptability.
3Measurement precision
If the system analyzes user behavior patterns to rank results, then search result accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-calculates field weights based on user access patterns before actual search operations. By performing this analysis in advance and storing the weighting criteria, the system avoids computationally intensive real-time analysis during search execution, thereby maintaining high accuracy while reducing processing time.
Solution Approach 2:
The system focuses analysis on the most significant fields and access patterns rather than processing all possible data elements. By identifying and prioritizing the most influential factors in user behavior, the model achieves high accuracy with reduced computational overhead, avoiding unnecessary processing of less relevant data.
Data Source
AI summary
A computer system is configured to receive a plurality of previous user selections by a user of previous database entries, each of which has as plurality of database field. The computer system is configured to determine weights for the various database fields included in the previous user selections and rank subsequent search results for a subsequent search of the database based on the determined weights, where the one or more weights affect a ranking of a search result based on a match associated with the particular database field. The computer system is further configured to receive customized search result layout settings specifying that one or more specified database fields are displayed to the user when the search results are displayed, where one or more weights for the particular database field are based on the customized search result layout settings.