Ranking Data Items Using Distance Functions for Continuous Scales
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Solution Overview
Problem
Existing data ranking mechanisms struggle to effectively rank large sets of data items, especially in search results, by combining object data attributes with user-dependent and dynamic contextual information, leading to suboptimal relevance and user experience.
Innovation Solution
A system that ranks data items based on object data attributes, seamlessly integrating discrete and continuous parameters, and dynamic contextual information, using distance functions and fuzzy searches to boost relevant items in search results, ensuring relevance to user queries and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ranking mechanisms are used to organize large sets of data items, then the basic ranking function is achieved, but the relevance to user queries and contextual appropriateness deteriorates
Solution Approach 1:
The ranking system is segmented into multiple independent components: object data attributes (discrete and continuous), user-dependent information, and dynamic contextual information. Each component is processed separately and then integrated through weighted combination, allowing complex ranking to be broken down into manageable segments that can be optimized independently.
Solution Approach 2:
The ranking system transitions from static traditional methods to dynamic ranking by incorporating real-time contextual information (location, time, device type) and user-dependent factors. The weights and parameters can be adjusted dynamically based on current context, enabling the system to adapt to changing user needs and environmental conditions.
2Adaptability or versatility
If simple ranking criteria are used, then the system complexity is reduced, but the ability to provide contextually appropriate results deteriorates
Solution Approach 1:
The ranking system is designed with universal applicability across different data types (discrete and continuous attributes) and various contextual scenarios. The same framework handles diverse attributes uniformly through standardized distance functions and weighting mechanisms, making the system versatile without requiring separate specialized mechanisms for each data type.
Solution Approach 2:
The system achieves contextual adaptability by changing parameters (weights, distance thresholds, significance values) based on dynamic context rather than changing the underlying ranking mechanism. This allows the same core system to adapt to different contexts by adjusting numerical parameters, balancing versatility with manageable complexity.
3Reliability
If multiple data attributes are considered in ranking, then the comprehensiveness of ranking improves, but the processing complexity increases
Solution Approach 1:
Different attributes are treated with locally optimized quality measures: discrete attributes use categorical matching with significance weights, while continuous attributes use distance-based similarity measures. Each attribute type receives tailored processing appropriate to its nature, improving ranking reliability without applying a one-size-fits-all complex algorithm to all attributes.
4Measurement precision
If fuzzy search and distance functions are implemented, then the relevance measurement accuracy improves, but the computational complexity increases
Solution Approach 1:
The system uses distance functions that compute similarity by measuring the 'distance' between query and data item attributes in a standardized metric space. This copying of mathematical distance concepts from geometry to information space provides intuitive and computationally efficient relevance measurement without requiring complex specialized algorithms.
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. Each ranking dimension comprises one or more object attributes and a ranking-impact weight, at least one of the object attributes being a continuous-scales 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 the one or more object attributes, a specified ranking-impact weight, the one or more values and associated significance weights, and a distance function corresponding to the continuous-scales object attribute. A database query for data objects of the object type and comprising the generated ranking condition is generated.