Computational Graph Reinforcement Learning with Feedback-Weighted Queries
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Solution Overview
Problem
Existing user query processing techniques on exchange platforms suffer from inaccuracies due to unbiased prior probability distributions for directional edges in computational graphs, failing to consider contextual information, user feedback, and cluster differences, leading to inefficient and inaccurate predictions.
Innovation Solution
Implementing a reinforcement learning model that utilizes user feedback to bias edge weights and employs random walk sampling on a computational graph, incorporating implicit feedback to enhance accuracy and efficiency by training a scoring function based on similarity measures and reducing computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unbiased prior probability distributions are used for directional edges in computational graphs, then the system is simpler to implement, but query processing accuracy deteriorates due to failure to consider contextual information and user feedback
Solution Approach 1:
The patent implements feedback mechanisms by incorporating user feedback (clicks, adds to cart, purchases) to update edge weights in the computational graph. The reinforcement learning model continuously learns from user interactions and adjusts the probability distributions, ensuring that the system adapts to actual user behavior patterns rather than relying on static unbiased priors.
Solution Approach 2:
The patent changes parameters by dynamically adjusting edge weights based on user feedback and contextual information. The reinforcement learning model modifies the probability distributions for directional edges, transforming them from static unbiased values to dynamic parameters that reflect actual user preferences and contextual relevance.
2Measurement precision
If reinforcement learning models with many parameters and non-linear operations are used for prediction, then query processing accuracy improves, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts the essential predictive function from complex reinforcement learning models by using a transition matrix that directly predicts inter-nodal transitions. This extraction eliminates unnecessary model parameters and non-linear operations while retaining the core predictive capability, achieving drastic reduction in computation cost.
Solution Approach 2:
The patent replaces expensive complex reinforcement learning models with a simpler transition matrix approach. The transition matrix serves as a lightweight, computationally efficient alternative that provides sufficient predictive accuracy without the burden of maintaining and computing with large numbers of model parameters.
3Measurement precision
If random walk sampling is performed on large computational graphs with billions of nodes and edges, then comprehensive coverage is achieved, but computational time increases significantly
Solution Approach 1:
The patent applies local quality by performing random walk sampling focused on local neighborhoods rather than exhaustive global traversal. The sampling process concentrates computational resources on relevant local regions of the computational graph, achieving accurate results without the need to process entire billion-node graphs.
Solution Approach 2:
The patent uses partial action by performing a limited number of random walk steps rather than exhaustive sampling. This partial sampling approach captures sufficient information for accurate query processing while avoiding the computational burden of complete graph traversal, achieving the right balance between coverage and efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that receives data representing a computational graph including multiple nodes and directional edges unidirectional connecting two neighboring nodes and receives training data including a reward function for a reinforcement learning model. A value function of the reinforcement learning model is initialized. For each node that is not a terminal node in the plurality of nodes, the value function of the reinforcement learning model is determined. The determination includes: updating the value function based on (i) a respective directional edge that starts from the node and connects a succeeding node and (ii) the reward function; determining that the updated value function converges, and in response, providing the updated value function as the value function. The value function for processing a user query is stored and used to process the user query.