Information Recommendation Feature Weighting Automation
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Solution Overview
Problem
Current information recommendation systems face challenges in accurately determining feature importance, leading to ineffective recommendations as the number of features increases, and rely on manual expertise, limiting scalability and transparency.
Innovation Solution
A method that determines a first set of weights for feature importance using feature representations and obtains a second set of weights, which are used to modify and recommend information, enhancing the accuracy and user experience by employing models like Logistic Regression and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual expertise is used to determine feature importance weights, then the recommendation system can be implemented, but the scalability and transparency are limited
Solution Approach 1:
The system automatically determines feature weights through machine learning models (Logistic Regression, neural networks) without requiring manual expert intervention. The models self-adjust weights based on training data, enabling the system to adapt to changing patterns in information recommendation while maintaining transparency through automated decision-making processes.
2Measurement precision
If the number of features is increased to improve recommendation accuracy, then more information can be considered, but the determination of feature importance becomes less effective
Solution Approach 1:
The system dynamically adjusts feature weights based on learned patterns from training data. Instead of using fixed manual weights, the machine learning models automatically determine optimal weight values for each feature, allowing the system to effectively handle a large number of features by adaptively prioritizing the most relevant ones for each recommendation context.
3Reliability
If traditional recommendation methods are used, then the system is simple to implement, but the recommendation effectiveness is reduced due to inability to accurately determine feature importance
Solution Approach 1:
The patent replaces manual expert-based weight determination (mechanical system) with automated machine learning models. Specifically, Logistic Regression and neural network models are used to automatically learn optimal feature weights from data, substituting human expertise with computational algorithms that can process and learn from large volumes of information more objectively and scalably.
Data Source
AI summary
A method, apparatus, device and storage medium for recommending information. The method includes determining, based on a set of feature representations of a plurality of features associated with information recommendation, a first set of weights indicating importance of the plurality of features. The method also includes determining a second set of weights based on the set of feature representations and the first set of weights. The method further includes recommending the information to a user based on the set of feature representations, the first set of weights and the second set of weights. The importance of respective features associated with the information recommendation can be accurately determined through this method, which further improves the effectiveness of information recommendation and improves the user experience.