Reinforcement Learning Model Inter-Session Rewards
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Solution Overview
Problem
Reinforcement learning models for content selection in online systems often prioritize short-term gains, leading to user experience degradation due to excessive or irrelevant supplemental content, as they fail to consider longer-term user engagement and inter-session effects.
Innovation Solution
A reinforcement learning model that incorporates inter-session rewards, characterizing the historical rewards of content compositions and penalizing longer time gaps between user interactions to optimize content selection, blending short-term and long-term engagement effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning models prioritize short-term rewards in content selection, then immediate user interaction and revenue are improved, but longer-term user engagement and experience deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing inter-session reward estimates in a cache before they are needed for decision-making. This allows the reinforcement learning model to access historical engagement data and long-term reward predictions without delaying the content selection process, thus maintaining immediate responsiveness while incorporating long-term engagement considerations into the reward function.
Solution Approach 2:
The patent introduces an inter-session reward component as an intermediary that bridges short-term and long-term objectives. This component computes rewards based on user engagement across multiple sessions and blends them with immediate interaction rewards, creating a composite reward signal that guides the reinforcement learning model to balance both short-term productivity and long-term reliability in content selection decisions.
2Power
If reinforcement learning models select content compositions based on bid values and near-term interactions, then revenue and short-term engagement are improved, but user experience and longer-term system usage deteriorate
Solution Approach 1:
The patent implements feedback by using inter-session rewards that capture user engagement patterns across multiple sessions. This feedback loop allows the system to learn from longer-term user responses to content compositions and adjust future selections accordingly. The inter-session reward signal provides feedback on whether high-bid content is actually beneficial for long-term user experience, enabling the model to refine its strategy to balance revenue generation with user satisfaction.
3Device complexity
If reinforcement learning models assume constant time between states, then computational complexity is reduced, but accuracy in modeling user behavior and session timing is worsened
Solution Approach 1:
The patent applies preliminary action by pre-computing inter-session reward values and caching them before they are needed for content selection. This approach allows the system to incorporate accurate, variable time intervals between user sessions without increasing the computational complexity of the real-time decision-making process. The pre-computed rewards capture precise timing information while keeping the online model computationally efficient.
Data Source
AI summary
A reinforcement learning model selects a content composition based, in part, on inter-session rewards. In addition to near-in-time rewards of user interactions with a content composition for evaluating possible actions, the reinforcement learning model also generates a reward and/or penalty based on between-session information, such as the time between sessions. This permits the reinforcement learning model to learn to evaluate content compositions not only on the immediate user response, but also on the effect of future user engagement. To determine a composition for a search query, the reinforcement learning model generates a state representation of the user and search query and evaluates candidate content compositions based on learned parameters of the reinforcement learning model that evaluates inter-session rewards of the content compositions.