Multi-task Neural Network for Content Prediction
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Solution Overview
Problem
Existing content customization systems in client-server environments face scalability issues and require large amounts of training data, leading to computational limitations and sub-optimal predictions, especially when dealing with a large number of content variations and users.
Innovation Solution
Implementing a multi-task neural network that predicts user responses for all content presentations using a single model, where user features are mapped to an input layer and content presentations are mapped to an output layer, allowing knowledge sharing across tasks and reducing the need for extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prediction models are implemented for different content variations, then prediction accuracy for each variation is improved, but system complexity and computational resources increase significantly
Solution Approach 1:
The patent combines multiple separate prediction models into a single unified prediction model that processes all content variations simultaneously. Instead of implementing individual logistic regression models for each web page variation, the system uses one prediction model that takes user features and content variation identifiers as inputs to predict conversion likelihood across all variations, thereby reducing system complexity while maintaining prediction accuracy
Solution Approach 2:
The prediction model is designed to be universal and multi-functional, capable of handling multiple content variations through a single model instance. The model accepts different content variation parameters and provides predictions for all variations without requiring separate model implementations, making the system more efficient and easier to maintain
2Measurement precision
If multiple prediction models are trained separately for each content variation, then prediction accuracy is improved, but training data requirements and processing time increase
Solution Approach 1:
The training process merges multiple separate model training operations into a single unified training process. Historical data containing user features, content variation identifiers, and conversion outcomes is used to train one prediction model that learns patterns across all content variations simultaneously, eliminating the need to collect and process separate training datasets for each variation and significantly reducing overall training time
3Productivity
If a single prediction model is used for all content presentations, then system scalability is improved, but prediction accuracy may be compromised
Solution Approach 1:
The patent introduces a new dimension to the prediction model by incorporating content variation identifiers as additional input features alongside user features. This dimensional expansion allows the single model to distinguish between different content variations and learn variation-specific patterns, maintaining prediction accuracy while achieving the scalability benefits of a unified model architecture
Data Source
AI summary
Techniques for predictively selecting a content presentation in a client-server computing environment are described. In an example, a content management system detects an interaction of a client with a server and accesses client features. Responses of the client to potential content presentations are predicted based on a multi-task neural network. The client features are mapped to input nodes and the potential content presentations are associated with tasks mapped to output nodes of the multi-task neural network. The tasks specify usages of the potential content presentations in response to the interaction with the server. In an example, the content management system selects the content presentation from the potential content presentations based on the predicted responses. For instance, the content presentation is selected based on having the highest likelihood. The content management system provides the content presentation to the client based on the task corresponding to the content presentation.