Social Network Member Rating System with Category Segmentation
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Solution Overview
Problem
Conventional social network websites do not allow members to uniquely rate or distinguish each other, limiting user interaction and relationship dynamics.
Innovation Solution
A system and method for rating members in a social network, enabling users to rate members in various categories, associating these ratings with the members, and influencing relationships, with overall ratings that can be recursively weighted based on associated members' ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional social network websites allow member interaction, then user engagement is maintained, but members cannot uniquely rate or distinguish each other
Solution Approach 1:
The rating system segments evaluation into multiple independent categories (e.g., trustworthiness, intelligence, fun, appearance) rather than using a single overall rating. This allows nuanced differentiation among members while maintaining system manageability through modular category design.
Solution Approach 2:
Different members can be rated differently across various categories, allowing unique distinction of individual characteristics. Each member receives a profile of category-specific ratings rather than a uniform evaluation, enabling precise differentiation of local qualities.
2Measurement precision
If websites allow users to rate other users, then user distinction is enabled, but the rating system becomes complex and difficult to manage
Solution Approach 1:
The complex task of comprehensive member evaluation is segmented into discrete, manageable categories. Each category represents a specific dimension of assessment, making the rating process more precise while reducing the cognitive and computational complexity compared to a holistic rating approach.
Solution Approach 2:
The system transforms the rating concept from a single scalar value into a multi-dimensional parameter set. By changing from one-dimensional to multi-dimensional parameter representation, the system achieves higher measurement precision while maintaining tractability through structured parameter organization.
3Reliability
If overall ratings are calculated from multiple member ratings, then comprehensive evaluation is achieved, but computational complexity increases
Solution Approach 1:
The calculation of overall ratings is segmented into category-level aggregations followed by composite synthesis. First, ratings are aggregated within each category independently, then category results are combined to form the overall evaluation. This segmented approach improves reliability through systematic aggregation while reducing computational complexity compared to simultaneous multi-dimensional calculation.
Solution Approach 2:
The system performs preliminary aggregation of ratings within each category before computing the final overall rating. This preliminary action organizes the data structure and reduces the computational burden of the final synthesis step, while ensuring reliable evaluation through systematic intermediate processing.
Data Source
AI summary
Systems and methods for rating associated members in a social network are set forth. According to one embodiment a method comprising outputting a ratings interface for rating at least one member of a social network associated with a user, wherein the rating interface provides the user with the ability to rate the member in one or more categories, receiving ratings for the member from the user, associating the ratings with the member, and connecting the ratings for the member with the user is set forth.