Quantum Recommendation System Using Decision Tree Feature Selection
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Solution Overview
Problem
Classical recommendation systems face inefficiencies and reduced accuracy when dealing with a large number of high-quality input features, as they struggle to perform real-time computations due to combinatorial growth in feature combinations.
Innovation Solution
A quantum recommendation system is developed, utilizing both classical and quantum computing technologies to construct a quantum decision tree that considers cumulative weighted averages of all nodes in the path, allowing for efficient real-time searches using Grover's algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical recommendation systems process a large number of high-quality input features, then recommendation accuracy is improved, but computation time increases exponentially due to combinatorial growth in feature combinations
Solution Approach 1:
The patent replaces the classical mechanical computation system with a quantum computing system that utilizes quantum mechanical principles (superposition and entanglement) to process recommendation features. This substitution enables parallel processing of multiple feature combinations simultaneously, resolving the contradiction between accuracy and computation time by maintaining high-dimensional feature processing capability while achieving exponential speedup in computation.
2Reliability
If classical systems use more input features for recommendations, then recommendation quality improves, but system complexity and processing overhead increase
Solution Approach 1:
The patent transitions from classical binary feature processing to quantum superposition states, adding a dimensional aspect to feature representation. By encoding features in quantum states that can exist in multiple configurations simultaneously, the system can process high-dimensional feature spaces without proportionally increasing classical processing complexity, as the quantum system naturally handles the complexity through its state space.
Data Source
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AI summary
Methods, systems, and apparatus for improving recommendation systems. In one aspect, a method includes obtaining training data including data sets, wherein each data set includes a value that corresponds to the target feature and multiple values that each correspond to a respective input feature of a set of input features; assigning an input feature from the set of input features to a root node of the quantum decision tree based on calculated information gain values for the input features; creating a path from the root node by iteratively: calculating a cumulative information gain value for unassigned input features; identifying a maximal cumulative information gain value for the unassigned input features and assigning the unassigned input feature corresponding to the maximal cumulative information gain value to a current leaf node in the path creating a new leaf node.