Social Network Recommendation Engine Ranking by Relationship Proximity
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Solution Overview
Problem
Existing online recommendation systems in e-commerce and social networks fail to effectively utilize relationship proximity to provide personalized and relevant item recommendations, relying mainly on general user interactions rather than leveraging the value of trusted relationships within social networks.
Innovation Solution
A system and method that obtain recommendations from members of a social networking utility, ranking them based on relationship proximity to the inquiring member, with friends being prioritized over strangers, and using a recommendation engine to display these recommendations in a tailored order, integrating with inventory management and e-commerce functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If recommendations are obtained from all members of a social networking utility regardless of relationship proximity, then the quantity of recommendations is maximized, but the relevance and authority of recommendations to the inquiring member deteriorates
Solution Approach 1:
The patent segments the social network members into different relationship proximity tiers (friends, acquaintances, strangers) and processes recommendations from each tier separately. This segmentation allows the system to maintain quantity by including all members while improving relevance by weighting and ranking recommendations according to their relationship proximity to the inquiring member, with friends receiving higher priority than strangers.
2Adaptability or versatility
If recommendations are ranked strictly by relationship proximity prioritizing friends over strangers, then the personalization and authority of recommendations is improved, but the diversity of recommendations from the broader network is reduced
Solution Approach 1:
The patent introduces a relationship proximity parameter that quantifies the strength of connection between the inquiring member and recommendation providers. By changing the parameter from a binary (friend/stranger) to a graduated scale based on interaction frequency and network distance, the system achieves fine-grained personalization without excessive complexity. The ranking algorithm uses this parameter to weight recommendations appropriately while maintaining computational efficiency.
3Productivity
If the system integrates relationship proximity ranking with e-commerce functionality, then the effectiveness of targeted marketing is improved, but the complexity of the system architecture increases
Solution Approach 1:
The patent merges the social networking relationship proximity assessment with the e-commerce recommendation engine into a unified system. Rather than maintaining separate systems for social interactions and commercial transactions, the invention integrates them by using the established relationship metrics from the social network to directly inform product recommendations and marketing targeting. This merging reduces overall system complexity while improving marketing effectiveness through better-targeted recommendations.
Data Source
AI summary
A method for making a recommendation, comprising obtaining a plurality of recommendations for a plurality of items from a plurality of members of a social networking utility, ranking the plurality of recommendations based on a relationship proximity of the plurality of members to an inquiring member within the social networking utility, wherein the relationship proximity is closest for friends of the inquiring member, and farthest for strangers to the inquiring member, wherein friends of the inquiring member are within a network of the inquiring member, and strangers of the inquiring member are outside of the network of the inquiring network, performing a search for one of the plurality of items, and displaying the plurality of recommendations associated with one of the plurality of items in an order defined by the ranking.