Omnichannel Content Selection via Channel-Specific Thompson Sampling
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Solution Overview
Problem
In omnichannel settings, existing approaches fail to effectively resolve the explore-exploit dilemma by ignoring channel-specific past-performance data, leading to sub-optimal content element selection and exploitation, particularly when users interact with both online and offline channels.
Innovation Solution
An automated system that tracks usage data across both online and offline channels, classifies users based on their conversion patterns, and selects content elements by generating random samples from posterior distributions specific to each channel, maximizing the weightage of effective content elements for personalized webpage content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing approaches are used for content element selection, then the system is simple to operate, but the conversion rates and user engagement are sub-optimal due to ignoring channel-specific past-performance data
Solution Approach 1:
The patent segments the omnichannel environment into distinct channels (online and offline) and maintains separate posterior distributions for each channel. This segmentation allows the system to capture channel-specific user behaviors and past-performance data, resolving the contradiction by enabling sophisticated conversion optimization without requiring complete redesign of the entire system.
Solution Approach 2:
The patent changes the parameter representation by using posterior distributions over conversion rates for each channel separately. Instead of a single aggregate conversion rate, the system maintains distinct parameters (posterior distributions) for online and offline channels, allowing more nuanced decision-making that improves conversion rates while managing complexity through probabilistic modeling.
2Measurement precision
If channel-specific past-performance data is considered, then the content element selection becomes more accurate, but the data processing and model complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and maintaining posterior distributions for each channel based on historical data. This preliminary processing of channel-specific past-performance data allows the system to make accurate content selection decisions in real-time without performing complex computations during the actual selection process, thus balancing accuracy with computational efficiency.
Solution Approach 2:
The patent implements feedback mechanisms by continuously updating posterior distributions with new conversion data from each channel. This feedback loop allows the system to learn from past-performance data and improve content selection accuracy over time, while the incremental nature of Bayesian updating keeps the data processing complexity manageable.
3Productivity
If Thompson sampling is used without channel differentiation, then the implementation is straightforward, but the exploitation of effective content elements is sub-optimal in omnichannel settings
Solution Approach 1:
The patent applies segmentation by creating separate Thompson sampling processes for each channel (online and offline) with distinct posterior distributions. This segmentation enables the system to exploit effective content elements more effectively within each channel context, resolving the contradiction by improving exploitation effectiveness while keeping each sampling model relatively simple and independent.
Solution Approach 2:
The patent adds a channel dimension to the Thompson sampling model, transforming it from a single-dimensional (aggregate) approach to a multi-dimensional (channel-specific) approach. This dimensional expansion allows the system to capture and exploit channel-specific patterns in user behavior, improving content exploitation effectiveness while maintaining the probabilistic framework's simplicity.
Data Source
AI summary
A method including tracking impression response data in response to online impressions of content elements displayed to users of a website. The method also can include performing a classification of the users based on the impression response data. The method additionally can include generating a webpage of the website to comprise a content element selected from among the content elements based on the classification of a user of the users. Other embodiments are described.