Recurrent Neural Network Next Command Recommendation
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Solution Overview
Problem
Existing techniques for recommending next commands in software application user interfaces, such as Bayesian and machine learning approaches, struggle to provide accurate and user-specific recommendations due to their inability to adapt to individual user characteristics like skill level and industry sector.
Innovation Solution
A trained recurrent neural network model, specifically a gated recurrent unit (GRU) neural network, is used to predict next commands by clustering final hidden states and producing separate predictions for different user characteristics, allowing for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bayesian approaches are used for next command recommendations, then the system can provide recommendations based on statistical probability, but the recommendations become too obvious or incorrect when forced to predict complex scenarios with very small probabilities
Solution Approach 1:
The patent changes the fundamental parameter of the recommendation system by switching from Bayesian statistical probability to neural network-based probability estimation. The neural network model processes user characteristic parameters (skill level, industry sector) and command sequence parameters to generate personalized recommendations, avoiding the mathematical constraints of Bayesian approaches while adapting to individual user patterns.
2Measurement precision
If prior machine learning models are used for next command recommendations, then the system can learn from data, but the accuracy remains insufficient and the models do not adapt to individual user characteristics
Solution Approach 1:
The patent applies local quality by creating separate recommendation predictions for different user characteristic groups. The neural network model generates distinct next command predictions based on user characteristics such as skill level and industry sector, ensuring that each user group receives customized recommendations tailored to their specific needs and patterns rather than a generic approach.
Solution Approach 2:
The patent segments the user base into different groups based on user characteristics (skill level, industry sector) and provides separate recommendations for each segment. The model processes user characteristic data to divide the prediction output into multiple specialized recommendation sets, allowing each segment to receive optimized recommendations relevant to their specific context.
3Device complexity
If a single unified recommendation model is used for all users, then the device complexity is reduced, but the recommendations cannot be adapted to individual user characteristics
Solution Approach 1:
The patent achieves universality through a single neural network model that performs multiple functions: it processes user characteristic inputs (skill level, industry sector), analyzes command sequence patterns, and generates personalized recommendations for different user groups. This multi-functional model maintains relatively low complexity while providing adapted recommendations across diverse user scenarios.
Data Source
AI summary
In example embodiments, techniques are provided for determining next command recommendations using a trained recurrent neural network model. A command prediction module of an application gathers command data and user characteristic data for a user, and cleans the command data to produce an input dataset. The command prediction module applies the input dataset to a trained recurrent neural network model, where the trained recurrent neural network model is configured to produce a separate next command prediction for each of a plurality of different values of one or more user characteristics. The command prediction module selects one or more recommended next commands from within the next command prediction produced for a value of one or more user characteristics that correspond to the user characteristic data for the user, and provides the one or more recommended next commands for display in a user interface of the application.