Geographically Localized Recommendations via Interestingness Filtering
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Solution Overview
Problem
Current online search tools for topical advice require expertise in using search engines and often produce voluminous results that are time-consuming to sift through, and static databases quickly become outdated, necessitating improved natural language-based search capabilities that can adapt and refine content continuously.
Innovation Solution
A computer-based advice facility that collects topical information, filters it based on an 'interestingness' aspect, determines an interestingness rating, and provides recommendations to users, using machine learning to optimize question selection and decision-making through user interaction and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines are used to search for topical advice, then comprehensive results can be obtained, but the results are voluminous and time-consuming to sift through
Solution Approach 1:
The patent extracts and filters only the most relevant and interesting information from the voluminous search results by introducing an 'interestingness' rating system. The advice facility collects topical information from multiple sources, evaluates each piece of information based on its interestingness aspect, and presents only the filtered, high-value content to users, thereby eliminating the need for users to manually sift through large volumes of results.
Solution Approach 2:
The patent introduces a new parameter 'interestingness rating' to evaluate and rank topical information. By changing the evaluation parameter from traditional search relevance to interestingness based on user profiles and current trends, the system transforms the presentation of results from a static list to a dynamically ranked set of information pieces, improving both efficiency and user satisfaction.
2Speed
If static databases of advice are used, then information can be quickly retrieved, but the databases quickly become outdated
Solution Approach 1:
The patent transforms the static advice database into a dynamic system that continuously collects, evaluates, and updates topical information from multiple sources. The advice facility maintains a living database that is regularly refreshed with new information and filtered through the interestingness rating system, ensuring that users access current and relevant advice while maintaining fast retrieval speeds through efficient data structures.
Solution Approach 2:
The system implements continuous information gathering and updating processes that operate in the background. Multiple information sources are continuously monitored, new topical information is constantly collected and evaluated, and the database is continuously refined. This continuous action ensures the advice remains current without requiring manual updates or sacrificing retrieval speed.
3Adaptability or versatility
If traditional search tools are used, then broad coverage of topics can be achieved, but expertise in using search engines is required
Solution Approach 1:
The advice facility performs the complex search and filtering operations automatically without requiring user expertise. The system self-manages the collection of topical information from multiple sources, the evaluation of interestingness, and the presentation of results. Users simply interact with the facility through simple queries or profile information, and the system handles the complex tasks of information retrieval, filtering, and ranking, making the process accessible to non-experts while maintaining broad topic coverage.
4Measurement precision
If personalized recommendations are implemented, then relevance to user interests improves, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with recommended information are tracked and used to refine the interestingness rating system. The advice facility learns from user behavior patterns, preferences, and engagement metrics to continuously improve personalization accuracy. This feedback loop enables the system to adapt to individual user interests over time while managing complexity through iterative learning rather than requiring complex upfront modeling.
Data Source
AI summary
The present disclosure provides a geographically localized recommendation to a user through a computer-based advice facility, comprising collecting a recommendation from an Internet source, wherein the recommendation is determined to have an interestingness aspect and a geographic location aspect, comparing the collected recommendation to a derived user taste and the user's current geographic location, determining at least one recommendation for the user based on processing on the comparison, and delivering at least one recommendation to a user's mobile communications device, wherein the user is enabled to at least one of view, save, and share the recommendation via an application at least in part resident on the computer-based advice facility.