Personalization Data Storage Service for Business Information
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Solution Overview
Problem
Consumers face difficulties in efficiently searching for, managing, and sharing business information due to the vast amount of data and the time required to find trusted and recommended businesses, leading to inconvenient and disordered processes.
Innovation Solution
A system and method that facilitate user selection and retention of business information, processing user data to recommend businesses based on individual preferences and location, using a personalization system with data storage and user-generated content management to streamline business information discovery and sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If consumers manually search for and manage business information, then they can find businesses of interest, but the process becomes time-consuming and disordered
Solution Approach 1:
The system performs preliminary actions by pre-organizing business information into collections based on themes and categories before users need to search. Users can browse pre-organized collections and receive recommendations without having to manually search through vast amounts of data, thus resolving the contradiction between search efficiency and time consumption.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user selections and behaviors to dynamically generate personalized recommendations. This feedback loop allows the system to learn from user interactions and improve recommendation accuracy over time, making the information discovery process more efficient and reducing the time users spend searching.
2Adaptability or versatility
If the system stores detailed business information for multiple users, then personalization quality improves, but data management complexity increases
Solution Approach 1:
The system segments business information into distinct collections organized by themes and categories. Each collection is independently structured with standardized schemas, allowing the system to manage complex data through modular organization. This segmentation enables personalized recommendations while keeping data management tractable through structured, reusable components.
Solution Approach 2:
The system creates universal collection templates that can serve multiple users and purposes. These standardized collection structures can be reused across different users and contexts, reducing the overall complexity of data management while still enabling highly personalized experiences. The same collection framework serves both storage and recommendation generation functions.
3Adaptability or versatility
If the system processes individual user selections and generates personalized recommendations, then service tailoring improves, but system processing requirements increase
Solution Approach 1:
The system performs preliminary processing by pre-organizing business information into themed collections and pre-computing relevant metadata before users need recommendations. This advance preparation reduces the computational burden during actual recommendation generation, allowing the system to provide personalized services without excessive processing requirements at query time.
Solution Approach 2:
The system applies local quality by tailoring recommendations to individual user contexts while using standardized collection structures. Each user receives personalized results based on their specific selections and profile, but the underlying collection frameworks remain consistent and efficiently manageable. This approach enables customization without proportionally increasing overall system complexity.
Data Source
AI summary
A data storage system or service is provided for the data that is generated by the personalization system. The data storage system can be configured to support storing, retrieving or querying, and updating of data such as user information, personalized content such as personalized business information and collection information, statistics information related to users, collections, businesses, and the like. The data model design of the data storage system may be configured to optimize performance associated with specific features of the personalized system such as following and/or sharing of collections. Additionally, the data storage system may be configured to detect and provide user notifications of trigger events.