Neural Fitted Q Iteration for Personalized Recommendation Policies
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Solution Overview
Problem
Existing recommendation systems face challenges such as 'cold start' issues, over specialization, and short-sightedness, where they struggle to provide relevant recommendations to new users and new content, and fail to optimize long-term user interactions.
Innovation Solution
A batch reinforcement learning approach using neural fitted Q-iteration, which models user interactions as a Markov decision process to learn an optimal policy that maximizes expected rewards, avoiding assumptions about the environment and data distribution, and optimizing for long-term user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional collaborative filtering or content-based filtering techniques are used, then recommendation systems can be implemented with simpler algorithms, but they suffer from cold start issues and over-specialization problems
Solution Approach 1:
The patent changes the fundamental parameters of the recommendation system by transitioning from traditional filtering methods to reinforcement learning with neural fitted Q-iteration. This involves changing the state representation, action space, and reward function parameters to model user interactions as a Markov decision process, enabling the system to handle cold start scenarios effectively
2Productivity
If recommendation systems optimize for immediate user interactions, then short-term engagement metrics improve, but they become short-sighted and fail to optimize long-term user value
Solution Approach 1:
The patent implements a feedback mechanism through the reinforcement learning framework where the system receives reward signals from user interactions and uses neural fitted Q-iteration to update its policy. This feedback loop enables the system to learn from both short-term interactions and long-term outcomes, optimizing for sustained user value rather than immediate engagement alone
Solution Approach 2:
The system performs preliminary action by using the trained neural network policy to predict optimal recommendations before user interactions occur. The offline training phase prepares the recommendation policy in advance, allowing the system to make informed decisions that balance short-term and long-term objectives without requiring real-time exploration
3Reliability
If offline batch reinforcement learning is used, then the system can learn from real-world interactions without simulators, but extensive data preprocessing is still required
Solution Approach 1:
The patent applies self-service by designing the neural fitted Q-iteration framework to automatically process and learn from raw interaction data without requiring extensive manual preprocessing. The system self-adjusts by collecting state-action-reward-next state tuples directly from real-world interactions and using the offline batch learning algorithm to train the policy, minimizing the need for external data preparation efforts
Data Source
AI summary
Machine learning techniques can be implemented to provide personalized websites. In an example, a group of training inputs is determined. A training input of the group of training inputs can comprise a current state that indicates a visit to a website via a user device, an action that indicates a recommendation rendered via the user device during the visit to the website, a user reward that indicates whether user input associated with a user identity via the user device interacted with the recommendation, and a next state that indicates a state of the website for the user device in response to the action being taken from the current state. The training inputs can be used for performing batch offline neural fitted Q iteration training on a neural network to produce a trained neural network. The trained neural network can be stored.