Deep Reinforcement Learning Skill Recommendation Model
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Solution Overview
Problem
Traditional recommendation models for online services rely on supervised learning, which is inefficient and fails to optimize long-term user engagement, often focusing on immediate interactions rather than encouraging users to complete their profiles for enhanced service value.
Innovation Solution
Implementing a deep reinforcement learning-based recommendation model that uses state and action embeddings to optimize user engagement by prompting users to add relevant skills to their profiles, leveraging a Markov decision process and reward functions to maximize both immediate and long-term user interaction metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning is used for recommendation models, then the model can be trained with available data, but the training process requires significant pre-processing and computation time, reducing system efficiency
Solution Approach 1:
The patent replaces traditional supervised learning mechanisms with reinforcement learning mechanisms. Instead of using static training datasets that require extensive pre-processing, the system employs an agent that learns through interaction with the environment, receiving rewards based on user engagement actions. This substitution eliminates the need for manual data pre-processing and reduces training time while maintaining recommendation effectiveness.
Solution Approach 2:
The patent introduces dynamic learning where the recommendation model continuously adapts through reinforcement learning. The system transitions from static supervised learning to dynamic reinforcement learning, where the model learns optimal recommendations through ongoing interaction with users and receives feedback in the form of rewards. This dynamic approach reduces the need for repeated pre-processing and enables the system to adapt to changing user preferences efficiently.
2Productivity
If traditional recommendation models focus on immediate user interactions, then they can optimize click-through rates, but they fail to optimize long-term user engagement and profile completion
Solution Approach 1:
The patent applies preliminary action by designing the reinforcement learning reward function to provide incentives for profile completion before users actually complete their profiles. The system proactively suggests skills and prompts users to add information to their profiles, creating preliminary conditions that lead to better long-term engagement. This proactive approach ensures that users have complete profiles before making recommendations, improving the reliability of long-term engagement.
Solution Approach 2:
The patent implements a feedback mechanism where the reinforcement learning agent receives rewards based on both immediate user interactions and long-term engagement metrics. The reward function is designed to balance immediate click-through rates with long-term profile completion and user engagement. This feedback loop enables the system to learn strategies that optimize both short-term and long-term objectives simultaneously, rather than focusing solely on immediate interactions.
Data Source
AI summary
Techniques for using deep reinforcement learning for training a recommendation model for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training a recommendation model using deep reinforcement learning and a Markov decision process, where the Markov decision process has a state space including state embeddings of a plurality of reference users, an action space including action embeddings of the plurality of reference users, and a reward function. The reward function may be configured to issue a first reward based on current impression interaction data and a second reward based on a measurement of engagement of the reference user with the online service.