Personalized Human Profile Ranking via Implicit Preference Learning
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Solution Overview
Problem
Conventional human profile ranking systems are inefficient as they require significant manual effort and do not account for selectors' biases, leading to inconsistent rankings across platforms and failure to consider implicit preferences.
Innovation Solution
A system that determines a personalized ranking function based on both explicit and implicit attributes preferred by the selector, using querying criteria and active learning techniques to infer and weight implicit attributes, thereby enabling accurate and customized ranking of human profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional profile ranking systems are used, then basic search functionality is provided, but significant manual effort is required and selectors' biases are not accounted for
Solution Approach 1:
The system implements feedback mechanisms by analyzing selectors' explicit preferences and implicit behaviors (such as profile viewing patterns, selection actions, and ranking adjustments) to continuously refine and personalize the ranking function, thereby automating the ranking process while adapting to individual selector biases
Solution Approach 2:
The system transforms the ranking process by changing parameters from static, generic ranking criteria to dynamic, personalized ranking functions that incorporate multiple attributes (explicit preferences and implicit behavioral signals) specific to each selector, enabling automated differentiation between selectors' unique biases
2Measurement precision
If generic ranking mechanisms are used, then implementation is simple, but rankings are inconsistent across platforms and fail to consider implicit preferences
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing selectors' behavioral patterns and preferences to build personalized ranking functions in advance, which then enable accurate and consistent ranking without requiring complex real-time computations during the actual ranking process
Solution Approach 2:
The system adds another dimension to the ranking process by incorporating implicit attributes (derived from behavioral data) alongside explicit preferences, creating a multi-dimensional personalized ranking function that improves accuracy while maintaining manageable complexity through structured analysis
Data Source
AI summary
The present subject matter relates to method(s) and system(s) to rank human profiles based on selection criteria personalized to a selector. In an embodiment, the method includes obtaining querying criteria from the selector to query a database comprising a set of human profiles. Further, a subset of human profiles is determined from the set of human profiles based on the querying criteria and a default ranking mechanism. Furthermore, a selection based ranking is obtained for the subset of human profiles. Further, based on the selection based ranking, a ranking function is determined that is indicative of a relative inclination of the selector towards the one or more implicit attributes. Such a determination is by capturing at least one implicit attribute in the ranking function from the selection based ranking. Further, the ranking function is applied to rank a fresh set of human profiles based on the ranking function.