Search Result Ranking With Reinforcement Learning Feedback
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Solution Overview
Problem
Conventional search result generation techniques lack the ability to effectively learn from user interactions without continuous ground truth data, leading to suboptimal user experience and engagement.
Innovation Solution
Employing reinforcement learning to generate search results by using a computing system that acts as an agent, interacting with a user interface to observe and learn from user interactions, adjusting content and layout based on rewards, allowing the system to improve search result effectiveness through experimentation and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search result generation techniques are used, then the system can provide search results, but the system cannot effectively learn from user interactions without continuous ground truth data
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with search results (clicks, dwell time, conversions) are collected and fed back to the reinforcement learning model. This feedback loop enables the system to continuously learn and improve search result generation without requiring external ground truth data, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The reinforcement learning model serves itself by generating its own training data from actual user interactions with search results. The system autonomously improves its performance by learning from real-world user behavior patterns, eliminating the need for continuous external ground truth data while maintaining and improving search result effectiveness.
2Adaptability or versatility
If reinforcement learning is employed to generate search results, then the system can learn and improve from user interactions, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary reward model that translates complex user interaction patterns into simplified reward signals for the reinforcement learning model. This intermediary layer manages the complexity by providing a clear objective function (maximizing reward) while capturing nuanced user preferences, enabling learning capability without proportionally increasing system complexity.
3Ease of operation
If the system optimizes content and layout for maximum rewards based on user interactions, then user engagement and satisfaction improve, but the computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user interaction data and pre-training the reward model offline. This allows the online search result generation to use a already-trained model that makes decisions based on pre-computed reward signals, reducing real-time computational resource consumption while maintaining high user engagement and satisfaction.
Data Source
AI summary
Techniques are described herein for reinforcement learning model based search result generation. An example method includes generating a search result by using a reinforcement learning model The method can include receiving a reward based on a state associated with the first search result. The method can include changing the weights of the reinforcement learning model. The method can further include generate a second search result using the reinforcement learning model based on the changed weights.