The online advertising platform serves as the intermediary between advertisers and users to facilitate the product trading process and generate profits. While the vitality of both the user and advertiser community is crucial to the long-term development of platforms, compared with the enduring efforts to improve user experience, little attention has been given to methods for attracting or retaining advertisers. To address this oversight, in this work, we investigate the first intelligent solution to the advertiser retention problem in online advertising. Motivated by advertiser surveys and data analytics, we first define a set of short-term ad performance metrics as the key retention metrics we need to optimize for advertisers. In consideration of the desirable properties of a retention framework, we propose an Auto-Coupon module to distribute impression-level virtual coupons to selected advertisers during the bidding process. We decompose the resulted retention optimization problem into two stages, and adopt feedback control as well as reinforcement learning methods to realize personalized coupon distribution. The offline experiments demonstrate the effectiveness of our methods in boosting the ad performance of churning advertisers, and the large-scale online A/B test further justifies the improvement in advertisers' willingness to stay in the platform.

Vertical federated learning (VFL) is an emerging collaborative machine learning paradigm to facilitate the utilization of private features distributed across multiple parties. During the inference process of VFL, the involved parties need to upload their local embeddings to be aggregated for the final prediction. Despite its remarkable performances, the inference process of the current VFL system is vulnerable to the strategic behavior of involved parties, as they could easily change the uploaded local embeddings to exert direct influences on the prediction result. In a representative case study of federated recommendation, we find the allocation of display opportunities to be severely disrupted due to the parties' preferences in display content. In order to elicit the true local embeddings for VFL system, we propose a distribution-based penalty mechanism to detect and penalize the strategic behaviors in collaborative inference. As the key motivation of our design, we theoretically prove the power of constraining the distribution of uploaded embeddings in preventing the dishonest parties from achieving higher utility. Our mechanism leverages statistical two-sample tests to distinguish whether the distribution of uploaded embeddings is reasonable, and penalize the dishonest party through deactivating her uploaded embeddings. The resulted mechanism could be shown to admit truth-telling to converge to a Bayesian Nash equilibrium asymptotically under mild conditions. The experimental results further demonstrate the effectiveness of the proposed mechanism to reduce the dishonest utility increase of strategic behaviors and promote the truthful uploading of local embeddings in inferences.

Data is becoming an important kind of commercial good, and many online marketplaces are set up to facilitate the exchange of data. However, most existing data market models and the corresponding pricing mechanisms fail to capture the unique economic properties of data. In this paper, we first characterize the new features of IoT data as a digital commodity, and then present a market model for IoT data exchange, from an information design perspective. We further propose a family of data pricing mechanisms for maximizing revenue under different information asymmetry settings. OurMSimplemechanism extracts full surplus for the model with one type of buyer in the market. When multiple types of buyers coexist, ourMGeneralmechanism optimally solves the problem of revenue maximization by formulating it as a convex program with polynomial size. For a more practical setting where buyers have bounded rationality, we design theMPracticalmechanism with a tight logarithmic approximation ratio. We also show that the seller can further increase revenue by offering a free data trial to the buyers. We evaluate our pricing mechanisms on a real-world ambient sound dataset. Evaluation results demonstrate that our pricing mechanisms achieve good performance and approach the optimal revenue.

Automated bidding, an emerging intelligent decision-making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. The intuition behind our design is to leverage personalized rank scores as the criteria to allocate items, and compute a critical ROI to transforms the constraints on budget to the same dimension as ROI. The experimental results demonstrate that the proposed auction mechanism outperforms the widely used ad auctions, such as first-price auction and second-price auction, in various automated bidding environments.