Bid Shading in The Brave New World of First-Price Auctions

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth. With great changes in how auctions are being organized, such as changing the second- to first-price auction type, advertisers and demand platforms are compelled to adapt to a new volatile environment. Bid shading is a known technique for preventing overpaying in auction systems that can help maintain the strategy equilibrium in first-price auctions, tackling one of its greatest drawbacks. In this study, we propose a machine learning approach of modeling optimal bid shading for non-censored online first-price ad auctions. We clearly motivate the approach and extensively evaluate it in both offline and online settings on a major demand side platform. The results demonstrate the superiority and robustness of the new approach as compared to the existing approaches across a range of performance metrics.Comment: In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20), October 19-23, 2020, Virtual Event, Irelan

Optimization Method for the Chiller plant of Central Air-conditioning System Parameters on Association Rules Analysis for Energy Conservation

More than 50% of the total energy consumption of central air-conditioning system is consumed by the central cooling plants. It is crucial to optimize central cooling plants operation parameter settings which is also significant for improving its operating efficiency, reducing the energy consumption, and promoting the overall energy saving of air-conditioning systems. The regular methods of central cooling plants optimization can be divided into three categories: engineering method, mechanism modeling and artificial intelligence modeling. In recent years, with the development of the internet of things, the monitoring and control platform for air-conditioning system provides data mining with mass ground truth data for central cooling plants optimization. Compared with the other methods, the data mining method for optimizing the key operation parameters of central cooling plants takes the advantages of simple, wide applicable and practical. In this paper, the association rule data mining method is proposed to optimize the operation parameters of the whole central cooling plants from the ground truth data. The central cooling plants in a shopping mall in Guangzhou is taken as the case study. Through historical data processing, like data cleaning, selection of optimization parameters, discrete transform of data and so on, the association rules are mined between the optimal energy efficiency Ratio and the running parameters of the central cooling plants under different operating conditions by Apriori algorithm. Finally, from the simulation results, it’s shown that by the association rules, the total energy consumption of the whole central cooling plants under two different working conditions are reduced 13.33% and 11.6% less than by the original operational parameters in the transition season and summer respectively. The simulation results verify the validity of the mining rules. This method excavates the energy saving potential of central cooling plants from the point of view of engineering practice, which is suitable for the central cooling plants which has accumulated a large amount of operation data and provides a reference for the energy-saving optimization operation of central cooling plants

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.Comment: SIGKD

AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct weighted sum pooling. However, these methods only manually select several fields from the target item side as the query to interact with the behaviors, neglecting the other target item fields, as well as user and context fields. Directly including all these fields in the attention may introduce noise and deteriorate the performance. In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields. Pruning on these field pairs via these learnable weights lead to automatic field pair selection, so as to identify and remove noisy field pairs. Though including more fields, the computation cost of AutoAttention is still low due to using a simple attention function and field pair selection. Extensive experiments on the public dataset and Tencent's production dataset demonstrate the effectiveness of the proposed approach.Comment: Accepted by ICDM 202