Risk-Aware Ad Policy Selection via Stochastic Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional risk-neutral approaches to selecting digital advertising recommendation policies fail to account for risk sensitivity, leading to potential catastrophic losses in businesses with limited resources or sensitive client bases, as they do not consider variability and risk tolerance in optimizing lifetime value.
Innovation Solution
A stochastic optimization theory is employed to select digital advertising recommendation policies that maximize expected lifetime value within a defined risk threshold, using a risk-tolerance value and confidence level, and reinforcement learning techniques to estimate gradients from user data, allowing for the identification of optimal policies that balance return and risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a risk-neutral approach is used to maximize expected average return, then lifetime value is optimized, but risk sensitivity is ignored leading to potential catastrophic losses
Solution Approach 1:
The patent transforms the policy selection problem from a conventional optimization task into a risk-aware selection process by changing the parameters considered: instead of only maximizing expected return, the system now selects policies based on matching risk profiles between the policy and the business, incorporating risk sensitivity as a key selection criterion
Solution Approach 2:
The patent introduces an intermediary mechanism (the risk-aware selection system) that mediates between the policy options and the business decision-makers. This intermediary evaluates policies not just on return metrics but on their risk characteristics and compatibility with business risk tolerance, thereby resolving the contradiction between optimization and risk sensitivity
2Reliability
If advertising policies are selected to maximize lifetime expected return, then long-term revenue is optimized, but short-term variability and risk are not controlled
Solution Approach 1:
The system changes the selection parameters from purely return-oriented metrics to include risk-profile matching. By evaluating policies based on their risk characteristics and comparing them against business risk tolerance, the system selects policies that provide stable, predictable returns rather than maximizing short-term gains at the expense of long-term stability
Solution Approach 2:
The patent applies beforehand cushioning by pre-evaluating and selecting policies whose risk profiles are compatible with business risk tolerance before implementation. This prevents catastrophic short-term losses by ensuring that even if variability occurs, it remains within acceptable bounds defined by the business's risk appetite
3Reliability
If conventional risk-neutral optimization is used, then expected return is maximized, but businesses with limited resources cannot sustain large-scale variability
Solution Approach 1:
The patent fundamentally changes the optimization parameters from expected return maximization to risk-profile matching. By selecting policies whose risk characteristics align with business constraints (limited liquid assets, sensitivity to variability), the system ensures business sustainability without requiring large asset buffers
Solution Approach 2:
The system incorporates feedback by continuously evaluating whether selected policies match the business's risk profile and resource constraints. This feedback mechanism ensures that policies are selected with consideration for business sustainability, adjusting selections based on the organization's specific resource limitations and risk tolerance
Data Source
AI summary
Systems and methods for selecting optimal policies that maximize expected return subject to given risk tolerance and confidence levels. In particular, methods and systems for selecting an optimal ad recommendation policy—based on user data, a set of ad recommendation policies, and risk thresholds—by sampling the user data and estimating gradients. The system or methods utilize the estimated gradients to select a good ad recommendation policy (an ad recommendation policy with high expected return) subject to the risk tolerance and confidence levels. To assist in selecting a risk-sensitive ad recommendation policy, a gradient-based algorithm is disclosed to find a near-optimal policy for conditional-value-at-risk (CVaR) risk-sensitive optimization.