Recommendation Models Using Adversarial Counterfactual Exposure Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommendation models do not account for the underlying exposure mechanism, leading to suboptimal recommendations and inconsistent detection of user preferences due to unknown exposure mechanisms, which affects the accuracy of supervised learning and evaluation methods.
Innovation Solution
Adversarial counterfactual learning and evaluation are employed to address the exposure mechanism uncertainty by converting the minimax problem into a two-model adversarial game, providing a principled approach that accounts for the unknown exposure mechanism and optimizing the recommendation model under worst-case scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation models are used, then the system is simple to implement, but the recommendation accuracy deteriorates due to not accounting for exposure mechanism
Solution Approach 1:
The patent segments the recommendation system into two distinct models: an exposure model that accounts for the exposure mechanism and a recommendation model that generates recommendations. This segmentation allows each model to specialize in its function, improving overall recommendation accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary exposure model that mediates between the recommendation model and the user feedback. This exposure model captures the underlying exposure mechanism, allowing the recommendation system to account for selection bias and improve measurement precision without requiring complete redesign of the entire system.
2Reliability
If exposure mechanism is not accounted for, then the model is easier to train, but user preference detection becomes inconsistent
Solution Approach 1:
The patent implements a feedback mechanism where the exposure model's predictions are used to adjust and reweight the training data for the recommendation model. This feedback loop allows the system to continuously improve user preference detection consistency by accounting for exposure biases identified in the training process.
Solution Approach 2:
The patent changes the parameters of the recommendation model by introducing exposure-aware weights and adjustments based on the exposure model's output. These parameter changes allow the model to compensate for exposure mechanism effects, improving reliability of user preference detection without requiring fundamental architectural changes.
3Measurement precision
If supervised learning is applied without exposure adjustment, then the training process is straightforward, but evaluation accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by first training the exposure model on historical data to capture exposure patterns before using it to adjust the recommendation model training. This preliminary exposure analysis enables subsequent supervised learning to be more accurate by correcting for biases in advance, improving evaluation accuracy while managing training complexity through staged processing.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include obtaining training data. The acts also can include training candidate recommendation models and an adversarial exposure model using the training data. The acts additionally can include generating recommendations based on a selected recommendation model of the candidate recommendation models. Other embodiments are described.