Message Diet Engine Optimizes Social Network Activity
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Solution Overview
Problem
Social networking services face challenges in determining the optimal number of messages to send to multiple member accounts while ensuring a desired level of activity and minimizing complaints, as existing methods lack efficiency in targeting specific user responses and complaints.
Innovation Solution
The Message Diet Engine uses machine learning models to select a minimum number of messages for each account, building Expected Activity and Expected Complaints models based on historical data to optimize message delivery, ensuring a target level of activity while avoiding complaints through a configurable and modular approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of messages are sent to member accounts to achieve desired social network activity, then activity level is improved, but the number of complaints increases
Solution Approach 1:
The system changes the parameter of message quantity from a fixed or uniform value to an optimized variable determined by machine learning models. The Message Diet Engine calculates the minimum number of messages needed for each member account based on historical data, transforming the message sending strategy from brute-force high-volume to precision-optimized low-volume, thereby reducing complaints while maintaining activity levels.
Solution Approach 2:
The system implements feedback loops where historical message data and resulting complaints are fed into machine learning models (Expected Activity Model and Expected Complaints Model). These models continuously learn from past outcomes and adjust future message recommendations, creating a closed-loop system that adapts to minimize complaints while achieving activity targets.
2Object-generated harmful factors
If the number of messages is reduced to minimize complaints, then complaint level is improved, but social network activity decreases
Solution Approach 1:
The system transforms the message quantity parameter from a reduced uniform value to an optimized variable. The Message Diet Engine determines the precise minimum number of messages needed for each member account, ensuring activity targets are met without excessive reduction. This parameter optimization allows the system to maintain activity levels while sending fewer messages overall.
Solution Approach 2:
The system performs preliminary analysis using machine learning models to predict the activity impact of different message quantities before actually sending messages. The Expected Activity Model estimates the social network activity that will result from recommended message counts, allowing the system to pre-determine optimal quantities that will achieve activity targets without trial-and-error sending.
3Productivity
If traditional message sending methods are used without optimization, then implementation simplicity is maintained, but message efficiency and complaint reduction capability are insufficient
Solution Approach 1:
The system introduces the Message Diet Engine as an intermediary component between message generation and message sending. This engine acts as a mediator that processes historical data, applies machine learning models, and outputs optimized message recommendations. The intermediary handles the complexity of optimization algorithms, keeping the core message sending system simple while achieving high efficiency through the specialized intermediate layer.
Solution Approach 2:
The system segments the message optimization problem into distinct functional components: data collection, Expected Activity Model, Expected Complaints Model, and optimization algorithm. Each component handles a specific aspect of the problem, allowing independent development and maintenance of each segment while working together to achieve overall message efficiency and complaint reduction.
Data Source
AI summary
A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Message Diet Engine that generates a pool of messages for a plurality member accounts of a social network service. Each message being of a respective message type from a plurality of message types and targeted to a specific member account. For each respective member account, the Message Diet Engine selects a minimum number of messages, from the pool of messages, targeted to the respective member account that prompts an expected social network activity target and avoids an expected number of complaints. Based on the selected minimum number of messages for each respective member account, the Message Diet Engine identifies a total minimum number of messages, from the pool of messages, to be sent to the plurality of member accounts that prompts an expected total social network activity target and avoids a total expected number of complaints.