Data Card Recommendation Engine Using Dimensional Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face frustration and inefficiency when searching for information due to the overwhelming amount of data available, requiring them to know specific search terms and navigate multiple sources, which can be time-consuming and burdensome.
Innovation Solution
A computer-implemented method and system that recommends relevant information by categorizing data into dimensions, matching and weighting these dimensions with user queries, and presenting recommended data cards based on user interaction, utilizing preference-based and content-based recommendation factors, including machine learning for personalized results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a user performs manual search using search engines, then the user can find information, but the user experiences frustration and time consumption due to not knowing optimal search terms and having to navigate multiple sources
Solution Approach 1:
The system automatically performs information discovery and generates recommendations without requiring user input. The recommendation engine autonomously categorizes data, determines dimensions, calculates weights, and presents relevant information cards to users, eliminating the need for users to manually construct search queries or navigate multiple sources.
Solution Approach 2:
The system pre-processes and categorizes available information into multiple dimensions before users need it. Data is organized into categories such as personal information, professional information, and contextual information in advance, so when a user requests information, the system can quickly retrieve and recommend relevant pre-organized data without requiring real-time search operations.
2Loss of information
If the system provides comprehensive information across multiple data cards, then information completeness is improved, but the complexity of presenting and navigating this information increases
Solution Approach 1:
The system changes the parameter of information organization by introducing a multi-dimensional categorization framework. Instead of presenting raw comprehensive data, the system transforms information into structured dimensions (personal, professional, contextual) with assignable weights, making complex information manageable and comparable through standardized parameters.
Solution Approach 2:
The recommendation engine acts as an intermediary between the comprehensive data repository and the user. It processes the complex information by categorizing data into dimensions, assigning weights to different categories, calculating combined weight totals, and filtering results to present only the most relevant information cards, thereby simplifying the interface between data complexity and user needs.
3Productivity
If the system uses automated recommendation algorithms, then information discovery efficiency is improved, but the requirement for processing and analyzing large datasets increases
Solution Approach 1:
The system applies partial action by not processing all available data equally. Instead of analyzing every data card comprehensively, the system categorizes information into dimensions and applies weighted scoring to focus processing on the most relevant dimensions. This selective processing approach maintains high recommendation efficiency while reducing overall computational energy requirements.
Data Source
AI summary
According to certain aspects of the disclosure, a computer-implemented method may be used for information discovery recommendation. The method may include receiving a query for a requested data card and determining information contained on a set of data cards other than the requested data card. Additionally, categorizing the information into a plurality of dimensions of data and matching the dimensions of data with information contained on the requested data card. Additionally, applying a weighting value to each of the matched plurality of dimensions of data and determining a combined weight total for each of the data cards. Additionally, determining at least one recommended data card with the highest combined weight total and displaying a user interface indicating at least one recommended data card is available. Additionally, presenting the at least one recommended data card based on a user interaction with the user interface.