Social Network Decision Board for Career-Aligned School Recommendations
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Solution Overview
Problem
Existing social network systems lack effective methods to provide comprehensive decision-making support for college-bound students in choosing schools that align with their career aspirations, relying on limited personal information rather than real-life career outcomes and professional networks.
Innovation Solution
A decision board platform within a social network system that aggregates member profile data to recommend schools and fields of study based on career outcomes, allowing users to input career aspirations and receive collective recommendations from professionals, with features like user interfaces for college and field of study pages, and a recommendation engine that normalizes and analyzes data to suggest relevant institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If social network systems use only personal profile information for recommendations, then the system complexity remains low, but the decision-making support quality is insufficient
Solution Approach 1:
The patent merges multiple data sources including personal profile information, career outcome data, and professional network information into a unified recommendation system. This combination of diverse data types enables comprehensive decision-making support while managing system complexity through integrated processing
2Reliability
If the system aggregates data from vast professional networks, then the recommendation quality improves, but the data processing complexity increases
Solution Approach 1:
The system extracts and isolates specific career outcome attributes and professional network features from the vast social network data. By extracting only the relevant career-related information needed for recommendations, the system maintains high recommendation quality while reducing unnecessary data processing complexity
Solution Approach 2:
The patent segments the large-scale social network data into manageable components including career outcome data, professional network connections, and skill information. This segmentation allows the system to process and analyze career-relevant data effectively without being overwhelmed by the full complexity of the social network
3Measurement precision
If multiple input data types are used for refinement, then the recommendation precision improves, but the information processing time increases
Solution Approach 1:
The system performs preliminary processing and organization of multiple data types including career outcomes, profile information, and network data before generating recommendations. By preparing and structuring the data in advance, the system achieves high recommendation precision while minimizing the time required during the actual recommendation generation process
Data Source
AI summary
A first member of a social network service provides a set of desired attributes and a designation of the type of a candidate having the desired attributes that is desired as a recommendation. The attributes of the profiles of other members of the social networks are searched for entities having the set of desired attributes. At least one of the entities having the desired attributes that result from the search is presented to the first member as the recommendation of a candidate. The first member may provide a second set of desired attributes and a designation of the type of at least one second candidate having the second set of desired attributes. The attributes of the profiles of the other members of the social network may be searched for second entities having the second set of desired attributes. The first member is presented with at least one of the second entities as the recommendation of a second candidate.