Prompt Notification Ranking via ML Probability
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Solution Overview
Problem
Social networking systems face challenges in effectively prompting users to engage with content, as existing methods lack personalized and optimized approaches to increase user interaction with notifications.
Innovation Solution
A machine-learning trained classifier is used to optimize a predictor function for ranking candidate prompt notifications based on posting and dismissal probabilities, selecting notifications that are likely to result in user engagement by analyzing user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional notification methods are used to prompt users, then the system can send notifications to users, but user engagement and interaction rates remain low
Solution Approach 1:
The system changes parameters by calculating posting probability and dismissal probability for each candidate prompt notification, then uses these probability parameters to rank and select notifications. This transforms the notification selection from a static process to a dynamic probability-based ranking system that adapts to user behavior patterns
Solution Approach 2:
The system implements feedback by logging user responses to sent prompt notifications and using this feedback data to retrain the machine learning classifier. This continuous feedback loop allows the system to learn from user interactions and improve notification ranking over time, directly addressing the low engagement problem
2Adaptability or versatility
If generic prompt notifications are sent to all users, then the notification system can operate simply, but personalized user engagement is insufficient
Solution Approach 1:
The system performs preliminary action by training a machine learning classifier on historical user behavior data before actual notification deployment. The classifier learns user preferences, posting patterns, and dismissal behaviors in advance, enabling personalized notification ranking when actual notifications are sent without requiring complex real-time analysis
Solution Approach 2:
The notification system serves itself by using its own logged response data to retrain and improve its classifier. The system automatically collects feedback from user interactions, retrains the machine learning model, and applies the improved model to future notifications, creating a self-improving personalized notification system
3Productivity
If multiple candidate prompt notifications are generated and ranked, then user engagement can be optimized, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by generating and ranking multiple candidate prompt notifications but only selecting and sending the top-ranked notification to each user. This approach maintains the benefits of comprehensive analysis (generating multiple candidates and ranking them) while avoiding the waste of processing and sending all candidates, thus reducing processing time and resource consumption
Data Source
AI summary
In one embodiment, a method includes accessing a number of candidate notifications stored on the social-networking system. Each of the candidate notifications includes a prompt to perform an action on the social-networking system. The method also includes generating a user feature vector quantifying features of the user; and assessing, using a machine-learning model, one or more feature vectors in order to calculate an interaction score for each of the candidate notifications. The one or more feature vectors includes the user feature vector. The method also includes ranking each of the candidate notifications based at least in part on the respective calculated interaction score; and providing, based at least in part on the ranking, one or more of the candidate notifications to a client device of the user. Each of the provided candidate notifications satisfies a pre-determined threshold value.