Search Profile Completion via Reference Trend Analysis
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Solution Overview
Problem
Online services, such as social networking, face inefficiencies due to incomplete user profiles, leading to omitted search results and increased electronic resource consumption, as users without certain data are not included in searches, affecting accuracy and completeness.
Innovation Solution
A computer system analyzes reference profiles with high search appearance data to identify trends in features and recommends these features to target profiles lacking them, enhancing their search visibility and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users have incomplete profile data, then the system requires less data storage and processing, but search accuracy and completeness deteriorate
Solution Approach 1:
The system proactively identifies missing profile data by comparing user profiles against ideal profiles and recommends specific data elements to collect before searches are performed. This preliminary data collection guidance ensures that profiles are completed in advance, improving search accuracy without requiring continuous data verification during search operations.
2Loss of information
If users spend more time on searches to find relevant results, then search completeness improves, but electronic resource consumption increases
Solution Approach 1:
The system performs preliminary analysis of user search behavior and profile completeness before searches are executed. By pre-identifying profiles that are likely to match search criteria based on existing data patterns, the system can prioritize and return relevant results faster, reducing the time users spend searching and thereby reducing electronic resource consumption while maintaining search completeness.
3Loss of information
If the system performs comprehensive searches including all potential matches, then search completeness improves, but processing time and computational expense increase
Solution Approach 1:
The system pre-processes and indexes profile data by identifying key features and attributes that are likely to be searched. This preliminary organization allows the search system to quickly filter and retrieve relevant results without performing exhaustive comparisons on all profiles, thereby maintaining search completeness while significantly reducing processing time.
4Measurement precision
If user profiles include more complete data, then search accuracy improves, but data storage and processing requirements increase
Solution Approach 1:
The system segments profile data into hierarchical categories (essential fields, optional fields, and supplemental fields) and processes only the relevant segments based on search criteria. This segmentation allows the system to maintain high search accuracy by focusing on pertinent data portions while reducing overall processing complexity by not continuously analyzing the entire profile dataset.
Solution Approach 2:
The system applies different processing quality levels to different portions of profile data based on their relevance to search queries. Critical profile elements receive thorough processing and validation to ensure search accuracy, while less critical elements receive minimal processing. This local quality approach optimizes the balance between search accuracy and processing complexity.
Data Source
AI summary
Techniques for reducing electronic resource consumption using search data are disclosed herein. In some embodiments, a computer-implemented method comprises: identifying a cohort of profiles from profiles based on a determination that at least one attribute is shared among the profile data of the cohort; receiving corresponding search appearance data including an impression count for the cohort of profiles; selecting reference profiles from the cohort based on the impression counts of the reference profiles; selecting a target profile from the cohort based on the impression count of the target profile; identifying a trend corresponding to at least one feature among the reference profiles; and causing an indication of the feature(s) to be displayed on a computing device of the user of the target profile based on the identifying of the trend.