The Guaranteed Delivery (GD) advertising is a crucial component of the online advertising industry, and the allocation of inventory in GD advertising is an important procedure that influences directly the ability of the publisher to fulfill the requirements and increase its revenues. Nowadays, as the requirements of advertisers become more and more diverse and fine-grained, the focus ratio requirement, which states that the portion of allocated impressions of a designated contract on focus media among all possible media should be greater than another contract, often appears in business scenarios. However, taking these requirements into account brings hardness for the GD advertising inventory allocation as the focus ratio requirements involve non-convex multilinear constraints. Existing methods which rely on the convex properties are not suitable for processing this problem, while mathematical programming or constraint-based heuristic solvers are unable to produce high-quality solutions within the time limit. Therefore, we propose a local search framework to address this challenge. It incorporates four new operators designed for handling multilinear constraints and a two-mode algorithmic architecture. Experimental results demonstrate that our algorithm is able to compute high-quality allocations with better business metrics compared to the state-of-the-art mathematical programming or constraint based heuristic solvers. Moreover, our algorithm is able to handle the general multilinear constraints and we hope it could be used to solve other problems in GD advertising with similar requirements.

Contemporary systems of Guaranteed Delivery (GD) advertising work with two different stages, namely, the offline selling stage and the online serving stage. The former deals with contract allocation, and the latter fulfills the impression allocation of signed contracts. Existing work usually handles these two stages separately. For example, contracts are formulated offline without concerning practical situations in the online serving stage. Therefore, we address in this paper a bi-objective contract allocation for GD advertising, which maximizes the impressions, i.e., Ad resource assignments, allocated for the new incoming advertising orders, and at the same time, controls the balance in the inventories. Since the proposed problem is high dimensional and heavily constrained, we design an efficient local search that focuses on the two objectives alternatively. The experimental results indicate that our algorithm outperforms multi-objective evolutionary algorithms and Gurobi, the former of which is commonly applied for multi-objective optimization and the latter of which is a well-known competitive commercial tool.

Guaranteed Delivery (GD) advertising plays an essential part in e-commerce marketing, where the ad publisher signs contracts with advertisers in advance by promising delivery of advertising impressions to fulfill targeting requirements for advertisers. Previous research on GD advertising mainly focused on online serving yet overlooked the importance of contract allocation at the GD selling stage. Traditional GD selling approaches consider impression inventory prediction and contract allocation as two separate stages. However, such a two-stage optimization often leads to inferior contract allocation performance. In this paper, our goal is to reduce this performance gap with a novel end-to-end approach. Specifically, we propose the Neural Lagrangian Selling (NLS) model to jointly predict the impression inventory and optimize the contract allocation of advertising impressions with a unified learning objective. To this end, we first develop a differentiable Lagrangian layer to backpropagate the allocation problem through the neural network and allow direct optimization of the allocation regret. Then, for effective optimization with various allocation targets and constraints, we design a graph convolutional neural network to extract predictive features from the bipartite allocation graph. Extensive experiments show that our approach can improve GD selling performance compared with existing two-stage approaches. Particularly, our optimization layer can outperform the baseline solvers in both computational efficiency and solution quality. To the best of our knowledge, this is the first study to apply the end-to-end prediction and optimization approach for industrial GD selling problems. Our work has implications for general prediction and allocation problems as well.

Real-time Bidding (RTB) and Guaranteed Display (GD) advertising are two primary ways to sell impressions for publishers in online display advertising. Although GD contract serves less efficiently compared to RTB ads, it helps advertisers reach numerous target audiences at a lower cost and allows publishers to increase overall advertising revenue. However, with billion-scale requests online per day, it’s a challenging problem for publishers to decide whether and which GD ad to display for each impression. In this paper, we propose an optimal allocation model for GD contracts considering optimizing three objectives: maximizing guaranteed delivery and impressions’ quality and minimizing the extra traffic cost of GD contracts to increase overall revenue. The traffic cost of GD contracts is defined as the potential expected revenue if the impression is allocated to RTB ads. Our model dynamically adjusts the weights for each GD contract between impressions’ quality and traffic cost based on real-time performance, which produces fairness-aware allocation results. A parallel training framework based on Parameter-Server (PS) architecture is utilized to efficiently and periodically update the model. Deriving from the allocation model, we also propose a simple and adaptive online bidding strategy for GD contracts, which can be updated quickly by feedback-based algorithms to achieve optimal impression allocation even in complex and dynamic environments. We demonstrate the effectiveness of our proposed method by using both offline evaluation and online A/B testing.

Guaranteed Display (GD) is widely used in e-commerce marketing for advertisers to acquire an agreed-upon number of impressions with target audiences. With the main objective to maximize the contract delivery rate under contract constraints, user interest (such as click-through rate and conversion rate) is also essential to improve the long-time return on investment for advertisers and the e-commerce platform. In this paper, we design an adaptive unified allocation framework (AUAF) by not only considering supply of audience impressions in request-level but also avoiding over-allocation of audience impressions. Specifically, our allocation model simultaneously optimizes the contract delivery and the match between advertisements and user interests with explicit constraint to prevent unnecessary allocation. Facing the challenge of serving billion-scale requests per day, a parameter-server based parallel optimization algorithm is also developed, enabling the proposed allocation model to be efficiently optimized and incrementally updated in minutes. Thus, the offline optimization results and the online decisions can be synchronized for real-time serving. In other words, our approach can achieve adaptive pacing that is consistent with the optimal allocation solution. Our extensive experimental results demonstrate that the proposed AUAF framework can improve both contract delivery rate and average click-through rate (CTR), which we use to measure the user interest in this paper. The improvements on CTR are statistically significant in comparison with existing methods. Moreover, since March 2020, AUAF has been deployed in the guaranteed display advertising system of Alibaba, bringing more than 10% increase on CTR without loss of contract delivery rate, which has resulted in significant value creation for the business.

Online display advertising has become a vital business for large-scale E-commerce markets. As the main goal of advertisers is to reach interested customer prospects, accurate conversion prediction is essential for successful online display advertising. A particular challenge for conversion prediction is that conversions may occur long after the click events. Such delayed feedback makes it a non-trivial task to keep conversion prediction models updated and consistent with the latest customer distribution. Although several studies have been conducted to tackle the delayed feedback issue, the relationship between the early conversion and full term conversion has not been fully exploited to improve conversion prediction. In this paper, we consider conversion prediction as a multi-task learning problem by leveraging multiple conversion labels after different observation intervals. Specifically, we propose a multi-task model with an end-to-end architecture for conversion prediction. Our approach is guided by theoretical and probabilistic analysis of the early and full term conversions. Our mixture-of-experts module can integrate distinct characteristics of input features and optimize the task-specific experts. In addition, the multiple tasks are jointly learned with a regularization term to ensure the embedding consistency between tasks and prevent potential overfitting issues. In comparison with competitive benchmarks, our approach can significantly improve conversion prediction with delayed feedback and improve business performance of online display advertising.