Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model for improving the performance at each individual scenario. Although the research on this task has made important progress, it still lacks the consideration of cross-scenario relations, thus leading to limitation in learning capability and difficulty in interrelation modeling.

To increase brand awareness, many advertisers conclude contracts with advertising platforms to purchase traffic and deliver advertisements to target audiences. In a whole delivery period, advertisers desire a certain impression count for the ads, and they expect that the delivery performance is as good as possible. Advertising platforms employ real-time pacing algorithms to satisfy the demands. However, the delivery procedure is also affected by publishers. Preloading is a widely used strategy for many types of ads (e.g., video ads) to make sure that the response time for displaying is legitimate, which results in delayed impression phenomenon. In this paper, we focus on a new research problem of impression pacing for preloaded ads, and propose a Reinforcement Learning To Pace framework RLTP. It learns a pacing agent that sequentially produces selection probabilities in the whole delivery period. To jointly optimize the objectives of impression count and delivery performance, RLTP employs tailored reward estimator to satisfy guaranteed impression count, penalize over-delivery and maximize traffic value. Experiments on large-scale datasets verify that RLTP outperforms baselines by a large margin. We have deployed it online to our advertising platform, and it achieves significant uplift for delivery completion rate and click-through rate.

Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-click behaviors such as dwell time and conversion decisions. To estimate CVR accurately and protect data privacy better, vertical federated learning (vFL) is a natural solution to combine two sides' advantages for training models, without exchanging raw data. Both CVR estimation and applied vFL algorithms have attracted increasing research attentions. However, standardized and systematical evaluations are missing: due to the lack of standardized datasets, existing studies adopt public datasets to simulate a vFL setting via hand-crafted feature partition, which brings challenges to fair comparison. We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. It contains a large-scale real world dataset collected from Alibaba's advertising platform, as well as systematical evaluations for both effectiveness and privacy aspects of various vFL algorithms. Besides, we also explore to incorporate unaligned data in vFL to improve effectiveness, and develop perturbation operations to protect privacy well. We hope that future research work in vFL and CVR estimation benefits from the FedAds benchmark.

Multi-task learning (MTL) has been widely applied in online advertising systems. To address the negative transfer issue, recent optimization methods emphasized the gradient alignment of directions or magnitudes. Since prior studies have proven that the shared modules contain both general and specific knowledge, overemphasizing on gradient alignment may crowd out task-specific knowledge. In this paper, we propose a transference-driven approach CoGrad that adaptively maximizes knowledge transference via Coordinated Gradient modification. We explicitly quantify the transference as loss reduction from one task to another, and optimize it to derive an auxiliary gradient. By incorporating this gradient into original task gradients, the model automatically maximizes inter-task transfer and minimizes individual losses, leading to general and specific knowledge harmonization. Besides, we introduce an efficient approximation of the Hessian matrix, making CoGrad computationally efficient. Both offline and online experiments verify that CoGrad significantly outperforms previous methods.

Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users’ cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users’ domain-specific preferences. In this paper we propose , a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users’ scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of for MDR.

Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple domains is effective to improve the overall performance. However, it is still challenging to improve generalization across domains under limited training data, and hard to deploy current solutions due to computational complexity. In this paper, we propose AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. We introduce domain-aware neuron-level weighting factors to measure the importance of neurons, with that for each domain our model can prune redundant neurons to improve generalization. We further add flexible sparsity regularizations to control the sparsity ratio of learned structures. Offline and online experiments show that AdaSparse outperforms previous multi-domain CTR models significantly.

Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation: for given users, the goal is to generate personalized bundle creatives that the users will be interested in. To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. Experiments on large-scale real-world datasets verify that our proposed model shows significant advantages in terms of creative quality and generation speed.

Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aims to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value -- is fine-grained and more practical. In this paper we propose a doubly-adaptive approach AdaCalib. It learns an isotonic function family to calibrate model predictions with the guidance of posterior statistics, and field-adaptive mechanisms are designed to ensure that the posterior is appropriate for the field value to be calibrated. Experiments verify that AdaCalib achieves significant improvement on calibration performance. It has been deployed online and beats previous approach.

In online advertising, conventional post-click conversion rate (CVR) estimation models are trained using clicked samples. However, during online serving the models need to estimate for all impression ads, leading to the sample selection bias (SSB) issue. Intuitively, providing reliable supervision signals for unclicked ads is a feasible way to alleviate the SSB issue. This paper proposes an uncertainty-regularized knowledge distillation (UKD) framework to debias CVR estimation via distilling knowledge from unclicked ads. A teacher model learns click-adaptive representations and produces pseudo-conversion labels on unclicked ads as supervision signals. Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels. Experiments on billion-scale datasets show that UKD outperforms previous debiasing methods. Online results verify that UKD achieves significant improvements.

Post-click conversion rate (CVR) estimation is a crucial task in online advertising and recommendation systems. To address the sample selection bias problem in traditional CVR models trained in click space, recent studies perform entire space multi-task learning based on the probability of events in user behavior funnels like "impression-click-conversion". However, those models learn the feature representation of each task independently, and omit potential inter-task correlations that can help improve the CVR estimation performance. In this paper, we propose AutoHERI, an entire space CVR model with automated hierarchical representation integration, which leverages the interplay across multi-tasks' representation learning. It performs neural architecture search to learn optimal connections between layer-wise representations of different tasks. Besides, AutoHERI achieves better search efficiency with one-shot search algorithm, and thus it can be easily extended to new scenarios that have more complex user behaviors. Both offline and online experimental results on large-scale real-world datasets verify that AutoHERI outperforms previous entire space models significantly.

In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-product based models with representation-focused architecture are commonly adopted to account for system efficiency. However, it brings a significant loss to the effectiveness of the system. In this paper, a novel pre-ranking approach is proposed which supports complicated models with interaction-focused architecture. It achieves a better tradeoff between effectiveness and efficiency by utilizing the proposed learnable Feature Selection method based on feature Complexity and variational Dropout (FSCD). Evaluations in a real-world e-commerce sponsored search system for a search engine demonstrate that utilizing the proposed pre-ranking, the effectiveness of the system is significantly improved. Moreover, compared to the systems with conventional pre-ranking models, an identical amount of computational resource is consumed.