Real-Time Bidding by Reinforcement Learning in Display Advertising (1701.02490v2)

Abstract: The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks.

• The paper shows that treating bidding as an MDP via reinforcement learning can significantly enhance click-through performance.
• The methodology employs neural networks to approximate value functions, achieving 16.7% and 7.4% improvements on iPinYou and YOYI datasets respectively.
• The research offers practical budget allocation benefits and paves the way for future end-to-end and model-free bidding optimization strategies.

Real-Time Bidding by Reinforcement Learning in Display Advertising

The paper "Real-Time Bidding by Reinforcement Learning in Display Advertising" addresses the challenge of optimizing bidding strategies for online display advertising using real-time bidding (RTB) mechanisms. The authors propose a novel framework where the bidding decision process is treated as a reinforcement learning problem, specifically modeled as a Markov Decision Process (MDP). RTB environments are highly dynamic, and the paper's approach aims to improve the allocation of campaign budgets by considering both immediate and future rewards, thus achieving optimal advertising performance.

Methodology

The authors formulate the problem by encoding each auction as a state in the MDP, where the state space includes auction information and real-time campaign parameters. An action within this framework is defined as the bid price for each impression. Utilizing neural networks, the authors tackle the scalability issues presented by large-scale real-world auction volumes. The state transition is modeled by auction competition, allowing for the development of a policy that seeks to maximize cumulative rewards over time.

The reinforcement learning-based bid optimization, termed as Reinforcement Learning to Bid (RLB), leverages dynamic programming for deriving the optimal bid amounts given a state, considering both potential user interactions and market competition. By fitting the differential values between states using neural networks, the proposed approach effectively approximates the value function, enabling its application to large-scale datasets.

Experimental Results

The empirical evaluation was conducted using two significant datasets: a large real-world dataset from iPinYou and an even larger dataset from YOYI. Results demonstrate that the proposed RLB method outperforms conventional methods such as static linear bidding strategies and previous MDP-based approaches. The RLB model achieved up to 16.7% improvement in click-through performance on iPinYou and a 7.4% gain on the YOYI dataset against the best-performing traditional method. Furthermore, online A/B testing in a commercial RTB platform exhibited a remarkable 44.7% increase in click performance compared to industry-standard methods.

Implications and Future Directions

Practically, the proposed method provides significant efficiency gains for advertisers, allowing more precise budget allocation in RTB systems. Theoretically, this research advances the application of reinforcement learning in complex decision-making environments beyond simple, static optimization cases.

Future directions of research could involve integrating end-to-end learning frameworks that unify utility estimation, bid landscape forecasting, and bid optimization into a cohesive system. Additionally, exploring model-free reinforcement learning approaches like Q-learning or policy gradient methods could potentially offer even more adaptive and responsive bidding strategies within fluctuating market environments.

The robust empirical results and the outlined methodologies of this paper constitute a significant contribution to the field of computational advertising, demonstrating the efficacy of reinforcement learning in optimizing complex, real-time decision processes in digital marketing environments.