Social Network Application Resource Allocation via User Affinity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social networking websites face challenges in managing the large number of applications available, leading to user overload and decreased user engagement due to excessive and unwanted notifications from applications, which can result in users ignoring or distrust of all communications, impeding the distribution of both abusive and non-abusive applications.
Innovation Solution
A computer-implemented method and system that calculates a user affinity score for each application based on user interactions, allowing for controlled channel resource consumption, where applications with higher user affinity scores receive better resource allocation and visibility, while those with low scores are restricted or disabled, promoting user-preferred content and minimizing unwanted information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If applications are allowed to freely use channels to communicate with members and promote themselves, then applications can increase their user base and functionality, but members become inundated with excessive information and notifications from applications
Solution Approach 1:
The system implements a feedback mechanism where member interactions with application notifications (clicks, ignores, dismissals) are tracked and used to calculate affinity scores. This feedback loop allows the system to dynamically adjust resource allocation based on actual member preferences, resolving the contradiction by enabling applications to grow their user base through targeted communications rather than blanket distribution.
Solution Approach 2:
The system changes the parameter of resource allocation from equal distribution to affinity-based distribution. By calculating affinity scores based on member interactions and using these scores to determine channel resource allocation, the system transforms the communication landscape from information overload to personalized, relevant notifications that members actually engage with.
2Productivity
If applications send frequent notifications and messages to increase visibility, then applications can attract more users, but members begin to ignore or distrust all communications including legitimate ones
Solution Approach 1:
The system uses member response behavior as feedback to determine communication frequency and affinity. When members engage positively with notifications, the system increases allocation to those applications; when members ignore or dismiss notifications, allocation is reduced. This feedback mechanism maintains member trust by ensuring communications are relevant and not excessive.
Solution Approach 2:
Instead of allowing all applications to send maximum notifications (excessive action), the system implements partial action by allocating channel resources proportionally to affinity scores. This ensures that only applications with demonstrated member interest receive full communication access, while others receive limited or no access, preventing communication fatigue.
3Adaptability or versatility
If the social networking website provides many channels for application communication, then applications have more ways to reach members, but the system resource consumption and member burden increase significantly
Solution Approach 1:
The system applies local quality by allocating different levels of channel access to different applications based on their affinity scores. High-affinity applications receive full access to multiple channels, while low-affinity applications receive restricted or no access. This localized differentiation optimizes resource consumption by directing system and member resources only to communications that are likely to be valuable.
Solution Approach 2:
The system implements partial action by not enabling all applications to use all channels simultaneously. Instead, channel resource allocation is partially granted based on affinity thresholds and scores, ensuring that the system manages communication load sustainably while still providing versatile communication capabilities to applications that earn them through member engagement.
Data Source
AI summary
Applications in social networks support interaction between members through various types of channels such as notifications, newsfeed, and so forth. For each channel, applications are ranked based on their user affinity measures. User affinity is based on measuring positive and negative interactions by users as both senders and recipients of messages generated by applications. Metrics are computed for the different types of messages and interactions provided by applications. For each channel, an application receives user affinity score based on specific weighted combination of the metrics. Applications use channel resources to send messages to increase their user base. Given the large number of applications that are available, the extent to which applications are allowed to use channels is controlled, limiting their resource consumption. User affinity scores of applications calculated for a channel are used to decide the allocation of channel resources for an application.