Social Network Recommendation Clusters for Out-of-Network Member Targeting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social network services face challenges in efficiently recommending relevant members to users while minimizing computational resources and messaging costs, especially when communicating with out-of-network members, due to limitations in connection degrees and spam prevention.
Innovation Solution
The system generates recommendation clusters by tracking user activities, identifying cluster categories based on shared attributes, and surfacing relevant member profiles, allowing for limited communication with out-of-network members through targeted recommendations, reducing unnecessary messages and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system recommends more out-of-network members to expand user connections, then the connectivity and networking value improves, but the computational resources and messaging costs increase
Solution Approach 1:
The patent segments the recommendation system into in-network and out-of-network components. In-network recommendations leverage existing connection data and shared attributes, which are computationally efficient. Out-of-network recommendations are limited and targeted, reducing the overall computational burden while maintaining connectivity benefits.
Solution Approach 2:
The system applies partial action by limiting out-of-network recommendations to specific conditions (e.g., when shared attributes are found). Instead of recommending all possible out-of-network members, it selectively recommends only those meeting predefined criteria, reducing computational resources while maintaining connectivity value.
2Ease of operation
If the system increases communication limits with out-of-network members, then the user interaction utility improves, but the risk of spam and unnecessary messages increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing trust criteria and shared attribute validation before allowing out-of-network communications. Members must meet predefined conditions (shared interests, mutual connections, professional affiliations) before being recommended, preventing spam before it occurs.
Solution Approach 2:
The system uses shared attributes and common connections as intermediaries between in-network and out-of-network members. These intermediaries validate the legitimacy of recommendations, acting as a filter that prevents spam while enabling useful interactions.
3Productivity
If the system sends more recommendation messages to out-of-network members, then the potential connections increase, but the messaging costs and network load increase
Solution Approach 1:
The system applies local quality by differentiating message delivery based on the recipient type. In-network members receive comprehensive recommendations, while out-of-network members receive limited, targeted recommendations only when specific conditions are met, reducing overall messaging costs while maintaining connection productivity.
Solution Approach 2:
The system changes the parameter of recommendation frequency and target selection based on network position. For out-of-network members, it adjusts the parameters to send fewer, more targeted messages based on shared attributes, reducing messaging costs while maintaining effective connection formation.
Data Source
AI summary
Techniques for generating recommendation cluster within a social network service are described. Consistent with some embodiments, sample members in a social network service are identified. The sample members may be associated with prior member activity involving a source member. A cluster category this then selected based on a member attribute shared by a plurality of the sample members. In turn, a recommendation cluster is generated based on the selected cluster category. Generating the recommendation duster may involve selecting member profiles that match the cluster category. The member profiles selected in this way form the recommendation cluster. One or more of the member profiles of the recommendation cluster are then surfaced to a client device operated by the source member.