Neural Network Pre-training for User Engagement Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current deep learning systems lack the ability to accurately predict human behavior, such as user interface engagement, which is crucial for optimizing website and application performance, as they struggle to effectively analyze and classify user interactions based on text and image inputs.
Innovation Solution
A computer-implemented method and system using a multi-layer neural network that combines pre-training with an unlabeled dataset and fine-tuning with a labeled dataset to estimate the probability of user interface object selection, leveraging techniques like bidirectional training and back-propagation to generate a classification vector for predicting user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning systems use traditional training methods with labeled datasets only, then training time and computational resources are limited, but prediction accuracy for user behavior is insufficient
Solution Approach 1:
The system performs preliminary pre-training on unlabeled datasets before fine-tuning on labeled datasets. This preliminary action allows the model to learn general patterns and features from abundant unlabeled data, reducing the time and computational resources needed during the subsequent fine-tuning phase while improving overall prediction accuracy for user behavior.
2Reliability
If deep learning systems use only labeled datasets for training, then training process is simpler, but the model's understanding of language and user behavior is limited
Solution Approach 1:
The training process is segmented into two distinct phases: pre-training on unlabeled datasets to build general language understanding, and fine-tuning on labeled datasets to specialize in user behavior prediction. This segmentation allows the model to develop comprehensive understanding while keeping each training phase manageable and purpose-driven.
Solution Approach 2:
The system performs preliminary pre-training on unlabeled datasets before fine-tuning on labeled datasets. This preliminary action allows the model to learn general patterns and features from abundant unlabeled data, reducing the time and computational resources needed during the subsequent fine-tuning phase while improving overall prediction accuracy for user behavior.
3Measurement precision
If the neural network model is trained comprehensively with both unlabeled and labeled datasets, then prediction accuracy improves, but computational resources and training complexity increase
Solution Approach 1:
The training process is segmented into two distinct phases: pre-training on unlabeled datasets to build general language understanding, and fine-tuning on labeled datasets to specialize in user behavior prediction. This segmentation allows the model to develop comprehensive understanding while keeping each training phase manageable and purpose-driven.
Solution Approach 2:
The system uses partial action by first training on a subset of unlabeled data for pre-training, then using a smaller labeled dataset for fine-tuning. This approach achieves good prediction accuracy without requiring exhaustive training on all available data, reducing computational complexity while maintaining effectiveness.
Data Source
AI summary
Methods and systems are provided for estimating the selection probability of a digital object on a website or application by a human based on features extracted from an image, a video, or input text description of the object by a user. A communication interface receives the input from the user. Memory is provided for storing a neural network model, selection probability prediction and training data, the training data including a first training dataset and a second training dataset. The neural network model is trained in a pre-training step with the first training dataset and is followed by a fine-tuning step with the second training dataset to obtain a multi-layer neural network. Input is provided to the multi-layer neural network to obtain a classification vector. Based on the classification vector, a selection probability prediction is calculated and delivered to the user through the communication interface.