Finite State Machine Content Delivery Optimization
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Solution Overview
Problem
Content service providers face a trade-off in optimizing user engagement metrics such as Daily Active Users (DAU) and Page Views (PV), as the characteristics optimal for DAU conversion often conflict with those optimal for PV increases, and there is typically only one delivery order of content that can be rendered to the end user.
Innovation Solution
The introduction of a state-machine based goal programming approach that dynamically switches the content delivery optimization target between DAU and PV based on the current state of the user, utilizing a finite state machine and switching module to select between separate objective models trained for DAU and PV optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single content delivery optimization model is used, then the system complexity is low, but the system cannot simultaneously optimize both DAU conversions and PV increases due to conflicting characteristics
Solution Approach 1:
The system dynamically switches between different optimization models (DAU model and PV model) based on the current user state determined by the finite state machine. This allows the content delivery optimization target to adapt to different user scenarios without requiring a completely new system design.
Solution Approach 2:
The optimization system is segmented into multiple specialized models (DAU optimization model and PV optimization model), each trained for a specific objective. The finite state machine segments the user states into different categories, routing to appropriate models based on the current state.
2Reliability
If separate models are trained for DAU and PV optimization, then optimization effectiveness for each metric improves, but compute overhead increases
Solution Approach 1:
The finite state machine automatically determines the user state and selects the appropriate model without requiring external intervention or complex decision-making logic. The system serves itself by having the state machine directly control model selection based on predefined state criteria.
Solution Approach 2:
The finite state machine acts as an intermediary between the user and the optimization models. It processes user state information and mediates the selection of the appropriate optimization model, simplifying the overall system architecture and reducing computational overhead compared to having multiple models compete or require complex ensemble methods.
3Manufacturing precision
If multiple optimization models are maintained, then content selection quality improves, but content service latency increases
Solution Approach 1:
The finite state machine pre-determines the user state and selects the appropriate optimization model before content delivery occurs. This preliminary action avoids runtime model switching or complex model selection processes that would increase latency, ensuring that the selected model is ready for immediate inference.
Data Source
AI summary
Aspects of the disclosure include methods and systems for optimizing content service through state-machine based goal programming. An exemplary method can include receiving, from a client, a card request for structured data cards and determining a state of the client. The method can include, based on the state of the client, selecting for inferencing, via a finite state machine, one of a first model and a second model, determining, from the respective model selected for inferencing, a ranking of a plurality of candidate structured data cards, and providing, to the client, a card response including one or more structured data cards of the plurality of candidate structured data cards according to the ranking.