Warm Starting Online Bandit Learners with Offline Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems using online learning models for recommendation systems are inefficient and inaccurate, particularly when dealing with large action spaces and new users, due to resource-intensive processing and struggles in learning the online environment.
Innovation Solution
The system employs offline models to warm start online bandit learner models, utilizing entropy reduction-based exploration to select actions and update the online model, thereby improving efficiency and accuracy by leveraging relevant offline models and historical observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional online learning models are used to select actions from a large action space, then the system can adapt to new users and environments, but the processing time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training offline bandit models on historical data before online deployment. These pre-trained models serve as warm starts for online learning, allowing the system to immediately leverage learned patterns from historical interactions without waiting for online exploration. This resolves the contradiction by performing adaptive learning in advance (improving adaptability) while reducing the computational burden during online action selection (reducing processing time).
Solution Approach 2:
The patent creates multiple copies of bandit models - both offline pre-trained models and online learning models. The system maintains an ensemble of models where offline models are copied and adapted for online use, allowing parallel processing and faster action selection. This copying approach enables the system to handle large action spaces efficiently by distributing the computational load across multiple model instances rather than relying on a single resource-intensive online model.
2Productivity
If conventional online learning models are used to generate recommendations, then the system can learn from online interactions, but the accuracy of recommendations decreases due to lack of relevant information
Solution Approach 1:
The patent merges offline pre-trained models with online learning models into a unified recommendation system. The offline models contribute learned patterns from historical data, while online models adapt to current user preferences. By combining these complementary sources of information, the system achieves both high learning efficiency from online interactions and high recommendation accuracy from historical knowledge, resolving the contradiction between productivity and measurement precision.
Solution Approach 2:
The patent introduces offline bandit models as intermediaries between historical data and online recommendation generation. These intermediary models process and filter historical information, providing refined features and patterns to the online learning process. This intermediary layer enhances recommendation accuracy by preprocessing historical knowledge while maintaining learning efficiency through the online model's continued adaptation to new interactions.
3Measurement precision
If more time is spent on action selection (exploitation), then the system can generate more accurate recommendations, but less time and resources remain for expanding the search space (exploration)
Solution Approach 1:
The patent implements periodic action by alternating between exploitation phases (using pre-trained offline models for accurate recommendations) and exploration phases (allowing online models to learn from interactions). This periodic switching enables the system to achieve high recommendation accuracy during exploitation while maintaining adaptability through scheduled exploration, resolving the contradiction between measurement precision and adaptability.
Solution Approach 2:
The patent performs preliminary exploration through offline pre-training on comprehensive historical data before online deployment. This preliminary action expands the search space in advance, allowing the online phase to focus on exploitation with smaller, more targeted exploration. The offline pre-training resolves the contradiction by expanding the search space beforehand (improving adaptability) while enabling more focused and accurate online recommendation generation (improving measurement precision).
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.