Business Discovery System with User Classification and Location Matching
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Solution Overview
Problem
Consumers face difficulties in efficiently searching for and managing business information, including finding trusted and recommended businesses, due to the complexity and time-consuming nature of existing systems.
Innovation Solution
A system and method that facilitate business discovery by allowing users to select and retain business information, classify users based on attributes, and match them with relevant business recommendations based on their location and preferences, using a computer system that processes user selections and location data to provide personalized business recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional business search systems are used, then comprehensive business information can be accessed, but the searching and management process becomes time-consuming and complex
Solution Approach 1:
The system performs preliminary actions by pre-classifying users into categories based on their attributes and pre-organizing business information into themed collections. When a user searches, the system has already prepared and structured the information according to user categories, eliminating the need for time-consuming manual searching and filtering during the actual search process.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user selections and behaviors to continuously refine and personalize business recommendations. The system learns from user interactions with business information, adjusting future recommendations to better match user preferences, thereby reducing the time needed to find relevant businesses while maintaining comprehensive information access.
2Adaptability or versatility
If manual business information management is used, then users can curate their own lists, but the process becomes disordered and inefficient
Solution Approach 1:
The system enables self-service by automatically performing user classification, business information organization, and recommendation generation based on user attributes and behaviors. Users benefit from personalized business lists and themed collections without manually curating them, as the system autonomously manages the organization and adaptation of business information according to each user's profile.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting user categories and business recommendations based on evolving user attributes and interactions. As user parameters (preferences, behaviors, demographics) change, the system automatically recalibrates the classification and reorganizes business information accordingly, maintaining both adaptability and operational ease without requiring manual intervention.
3Productivity
If generic business recommendations are provided, then broad coverage is achieved, but personalized and relevant recommendations are lacking
Solution Approach 1:
The system applies local quality by providing personalized business recommendations tailored to each user's specific category, attributes, and location rather than generic recommendations for all users. The business information is organized into themed collections that are locally optimized for each user segment, ensuring that users receive highly relevant personalized recommendations while the system maintains broad coverage across multiple user categories and business types.
Data Source
AI summary
Systems, methods, and computer-readable media are provided to assist a user to identify one or more businesses of interest. Individual selections of businesses by end users to retain information corresponding to the businesses may be facilitated. Collections of business information may be retained in a repository. Each end user may be classified into a set of categories based on attributes. A first end user may be matched to a first category. A first set of collection information associated with a first set of end users that correspond to the first category and the first location may be identified. A recommendation for the first end user may be determined based on the first set of collection information, the first category, and the first location.