Recommendation Parameter Tuning for Long-Term User Revenue
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Solution Overview
Problem
Current recommendation systems fail to effectively optimize long-term behavioral revenues of users, as they often prioritize short-term gains and lack adaptability, leading to content singularity and inefficiencies.
Innovation Solution
The method employs reinforcement learning to optimize recommendation system parameters by treating the system as an agent interacting with users as an environment, where long-term behavioral revenues are the reward, allowing for iterative updates to maximize user engagement and retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current recommendation systems prioritize short-term gains, then immediate performance metrics improve, but long-term behavioral revenues deteriorate
Solution Approach 1:
The patent implements dynamic parameter adjustment through reinforcement learning, where the recommendation system continuously adapts its parameters based on real-time user feedback and long-term revenue signals. The system transitions from static short-term optimization to dynamic long-term optimization by learning from ongoing user interactions and adjusting recommendations to maximize cumulative revenue over time.
2Ease of manufacture
If recommendation systems use traditional supervised learning or artificial rules, then implementation simplicity is maintained, but adaptability to user preferences deteriorates
Solution Approach 1:
The patent implements self-service through reinforcement learning, where the recommendation system automatically learns and adapts to user preferences without requiring manual rule configuration. The system autonomously optimizes its parameters by receiving rewards based on long-term behavioral revenues, eliminating the need for complex人工 rule setting while maintaining high adaptability to changing user preferences.
3Stability of the object's composition
If recommendation systems focus on content singularity, then recommendation consistency improves, but user engagement and retention deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors user interactions with recommended content and uses this feedback to adjust future recommendations. By incorporating long-term behavioral revenue signals as reward feedback, the system learns to balance consistency with diversity, maintaining stable recommendation quality while adapting to user preferences that drive engagement and retention.
Data Source
AI summary
A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.