Paper: Deep RL for Influence Maximization in Social Networks

The problem of Influence Maximization (IM) was first formalized as a discrete optimization problem and proven to be NP-hard. Early solutions, like the greedy algorithm, provided a guaranteed approximation ratio for identifying influential nodes but often relied on static network models. Traditional IM algorithms often overlook the dynamic nature of social networks, where user attributes, interests, and connections constantly evolve. These older models struggle to adapt to the real-time, interactive feedback loops characteristic of modern content platforms. Reinforcement learning (RL) frames recommendation as a sequential decision-making process, aiming to maximize long-term user engagement rather than myopic, immediate rewards. This approach allows a system to learn a policy that adapts to changing user preferences and helps users discover new interests over time. Deploying RL in production at a company like YouTube presents significant engineering challenges due to the massive scale. The "action space," or the catalogue of potential videos to recommend, can be in the order of millions or billions, far exceeding the scale of typical RL applications in games or robotics. A primary challenge is the need for safe and efficient exploration. Showing users completely random content to gather data would create a poor user experience, making off-policy evaluation—learning from historical user interaction logs—a critical necessity. This reliance on logged data introduces its own biases, and a key research area is developing algorithms that can effectively learn and correct for these biases. Furthermore, DRL models can be computationally expensive and data-inefficient, requiring vast amounts of interaction data to train effectively, a major hurdle for real-world deployment. At an industrial scale, RL is used to optimize for complex, multi-objective reward functions that might include user retention, session duration, and fairness considerations, not just simple clicks or views. The goal is to move beyond pointwise filtering and learn a recommendation policy that considers the long-term impact of each piece of content.