Dynamic Contextual Ranking for Search Relevance
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Solution Overview
Problem
Existing ranking mechanisms struggle to effectively rank data items in search results based on dynamic contextual information, such as user location and temperature, leading to irrelevant results.
Innovation Solution
A system that utilizes dynamic contextual information to influence ranking by integrating it into the ranking configuration, allowing data objects to be boosted or lowered in rank based on parameters like user location and temperature, combined with traditional ranking criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ranking criteria are used, then ranking simplicity is maintained, but relevance to dynamic contextual information deteriorates
Solution Approach 1:
The patent implements dynamic ranking by allowing ranking criteria to change based on contextual information such as user location, time of day, and weather conditions. The ranking mechanism transitions from static to dynamic by incorporating contextual variables that can vary over time, enabling the system to adapt rankings to current conditions while maintaining core functionality.
Solution Approach 2:
The patent changes ranking parameters based on contextual information. Instead of using fixed ranking criteria, the system adjusts parameters such as relevance weights, sorting orders, and filtering thresholds according to dynamic contextual factors like user location, time, and environmental conditions, thereby improving result relevance without requiring complete system redesign.
2Reliability
If dynamic contextual information is integrated into ranking, then relevance of search results is improved, but ranking computation time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching contextual information where possible, such as storing user location data, time zone information, and frequently accessed contextual parameters. This allows the ranking system to retrieve pre-processed contextual data rather than computing everything in real-time, reducing computation time while maintaining dynamic adaptability.
Solution Approach 2:
The patent introduces an intermediary layer between the ranking algorithm and contextual information sources. This intermediary component handles the complexity of contextual data retrieval, processing, and integration, shielding the main ranking computation from direct complexity and enabling more efficient processing by abstracting away the details of contextual data access.
3Measurement precision
If multiple ranking criteria are combined, then ranking precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine and adjust ranking criteria based on contextual information without requiring manual user configuration. The system autonomously selects relevant contextual factors, weights them appropriately, and applies them to rankings, freeing users from complex configuration tasks while maintaining high precision through adaptive, context-aware ranking.
Data Source
AI summary
Methods, systems, and computer program products for ranking data items are described. A ranking configuration is defined for an object type, the ranking configuration comprising an assignment of one or more ranking dimensions to the object type and, for each of the one or more assigned ranking dimensions, one or more corresponding values and a significance weight assigned to each value, with at least one of the values being a dynamic value. Each ranking dimension comprises one or more object attributes and a ranking-impact weight, with at least one of the object attributes being a context-based object attribute. A ranking condition is generated for each ranking dimension assigned to the object type. The generation of the ranking condition is based at least in part on one or more object attributes, a specified ranking-impact weight, a context value, and the one or more values and associated significance weights. A database query for data objects of the specified object type and comprising the generated ranking condition is generated.