Two-Sided Utility Network Connection Ranking
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Solution Overview
Problem
Existing online network systems face challenges in efficiently utilizing resources to facilitate new connections between entities, as the utility of new connections often reaches a point of diminishing returns in improving key performance indicators, and existing methods struggle to accurately rank recommendations based on two-sided utility.
Innovation Solution
A machine-learned model is developed to determine the utility of new connections for both initiators and receivers, combining predictions of key performance indicators like engagement and sessions, and adjusting rankings based on a two-sided utility value, which considers the impact on both entities involved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new connections are facilitated for each entity to improve key performance indicators, then engagement and session counts increase, but resources (processing power, memory, bandwidth) are inefficiently utilized due to diminishing returns
Solution Approach 1:
The system changes the parameter of connection recommendation by introducing a two-sided utility score that dynamically adjusts recommendations based on both initiator and receiver characteristics. This optimization ensures resources are allocated to connections with highest predicted utility, avoiding diminishing returns while maintaining KPI improvement
Solution Approach 2:
The system implements feedback mechanisms by continuously learning from actual connection outcomes and utility realizations. This feedback loop allows the system to refine resource allocation decisions, directing processing power and bandwidth toward connection types that demonstrably improve KPIs without wasting resources on low-value connections
2Device complexity
If connection recommendations are made without considering two-sided utility, then resource allocation is simplified, but the accuracy of ranking recommendations is reduced
Solution Approach 1:
The system performs preliminary actions by pre-computing utility scores and ranking recommendations before actual connection requests occur. This advance preparation using machine learning models ensures high ranking accuracy is achieved without adding complexity to the real-time recommendation delivery mechanism
3Ease of manufacture
If traditional single-sided utility ranking is used, then the recommendation system is easier to implement, but it fails to capture the full value of connections for both entities
Solution Approach 1:
The system merges the utility assessment perspectives of both initiators and receivers into a unified two-sided utility score. This combination captures complete connection value information while maintaining implementation feasibility by using standardized machine learning techniques that build upon traditional single-sided approaches
Data Source
AI summary
Operations for facilitating establishment of connections in an online network are disclosed. A set of connection recommendations for a first entity associated with the online network is accessed. For each connection recommendation in the set of connection recommendations, a ranking value associated with the connection recommendation is accessed, a utility value corresponding to the connection recommendation is determined, and an adjusted the ranking value for the connection recommendation is calculated. The utility value is a two-sided utility value that combines a prediction of a utility of the first entity and a prediction of a utility of a second entity with respect to a key performance indicator. A set of connection recommendations is communicated for presentation in an interactive user interface of a client device associated with the first entity in accordance with the adjusted ranking value of each connection recommendation.