Online Prediction Model Training via Offline-Online Feature Segmentation
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Solution Overview
Problem
Current recommendation systems face challenges in effectively utilizing offline and online user data to generate accurate and personalized product recommendations, as they often fail to leverage real-time user behavior data for fine-tuning models, leading to suboptimal prediction performance.
Innovation Solution
The method involves acquiring offline and online sample features, offline training to obtain a recommendation model, and online training to adapt real-time data for input into the offline model, synchronizing the offline model online to generate an online prediction model, thereby optimizing the recommendation engine with real-time user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only offline sample features are used for model training, then model stability is maintained, but prediction accuracy and responsiveness to real-time user behavior deteriorate
Solution Approach 1:
The patent segments the training process into two distinct phases: offline training using historical sample features to ensure model stability, and online training using real-time user behavior data to improve prediction accuracy. This segmentation allows each phase to serve its specific purpose without compromising the other
Solution Approach 2:
The patent implements a dynamic model updating mechanism where the recommendation model is continuously refined through online training after initial offline training. This dynamic approach enables the model to adapt to changing user preferences and real-time behavior patterns while maintaining the foundational stability established during offline training
2Measurement precision
If real-time online data is continuously integrated into the model, then prediction accuracy improves, but computational complexity and training time increase
Solution Approach 1:
The patent performs preliminary offline training to establish a baseline recommendation model before deploying online training. This preliminary action reduces the computational burden during online operations, as the model only needs to perform incremental updates rather than complete retraining, thereby lowering real-time computational complexity
Solution Approach 2:
The patent implements continuous online training that operates in parallel with recommendation generation, allowing the model to learn from new user interactions without interrupting service. This continuous learning approach improves prediction accuracy over time while distributing computational load, preventing peak complexity during high-traffic periods
3Productivity
If offline recommendation model is directly deployed online, then deployment speed is fast, but adaptability to latest user behavior patterns deteriorates
Solution Approach 1:
The patent creates a dynamic model evolution system where the recommendation model transitions from static offline training to dynamic online learning. The model continuously adapts to new user behavior patterns through online training while maintaining the deployment efficiency of the pre-trained offline model, achieving both fast deployment and high adaptability
Solution Approach 2:
The patent implements a feedback mechanism where online user interactions are continuously fed back into the model through online training. This feedback loop enables the model to learn from actual user behavior patterns and adjust its recommendations accordingly, significantly improving adaptability while maintaining rapid deployment through the pre-trained offline model
Data Source
AI summary
A method and apparatus for training an online prediction model are provided. The method may include: acquiring an offline sample feature and an online sample feature of a user, the offline sample feature including a user portrait feature; offline training to obtain an offline recommendation model, based on the offline sample feature and the online sample feature of the user; acquiring a latest online feature of the user, and online training to obtain an online learning model based on the latest online feature of the user, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model; and synchronizing the offline recommendation model to online, and inputting the latest online feature output by the online learning model into the offline recommendation model to generate an online prediction model.