Thompson Sampling for Dynamic Content Selection
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Solution Overview
Problem
Existing automated decision engines face challenges in resolving the explore-exploit dilemma in omnichannel settings, particularly when considering seasonality and personalization, as they often ignore channel-specific differences and static user preferences, leading to sub-optimal content selection and neglecting evolving user preferences.
Innovation Solution
An automated system that employs a distributed network architecture to track user behavior across channels, classify users based on their channel preferences, and use posterior distribution sampling to select content elements that balance exploration and exploitation, while also applying a temporal decay factor to revive underperforming content elements in evolving environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If automated decision engines use static user preferences and ignore channel-specific differences, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent segments users into distinct groups based on their channel preferences and behaviors. By classifying users into segments with similar characteristics, the system can apply different content selection strategies to each segment, improving adaptability without requiring complex individualized models for every user.
Solution Approach 2:
The patent implements dynamic user preference modeling that evolves over time. The system continuously updates user preference representations based on new interactions and feedback, allowing the decision engine to adapt to changing user behaviors while maintaining a manageable complexity through structured update mechanisms.
2Productivity
If the system focuses only on exploiting known effective content, then productivity is improved, but adaptability deteriorates
Solution Approach 1:
The patent employs Thompson sampling, which uses probabilistic parameter updates to balance exploration and exploitation. By maintaining probability distributions over content effectiveness parameters and sampling from these distributions, the system can dynamically adjust between exploiting known effective content and exploring potentially better options based on current uncertainty levels.
3Device complexity
If the system does not consider temporal decay of content performance, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements periodic reassessment of content effectiveness by applying temporal decay factors. The system regularly updates content performance evaluations based on recency, allowing previously effective content to have reduced influence over time while giving fresh content opportunities to prove their effectiveness, thus adapting to evolving user preferences.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: displaying content elements on one or more websites to users; performing a classification of the users into segments; receiving a request from a first user of the users to display a webpage of the one or more websites; determining an amount of information in a respective posterior distribution of each of the one or more first segments of the segments in which the first user is classified, for each of the content elements based on impression response data; selecting a selected content element from among the content elements based on weightings of the content elements for the one or more first segments; and generating the webpage comprising the selected content element. Other embodiments are disclosed.