Online advertising has been the mainstream monetization approach for internet-based companies, in which bid optimization plays a crucial role in enhancing advertising performance. Currently, the bid optimization problem has narrowed down to two specific forms: Budget-constrained bidding (BCB) and Multi-constraint bidding (MCB). Existing solutions try to solve BCB/MCB via linear programming solvers, learning methods, or feedback control. However, in large-scale complex e-commerce, they still suffer from inefficiency, poor convergence, or slow adaptation to the changing market. This research presents an online receding optimization method as a solution for practical bid optimization problems. We conduct a theoretical analysis of the optimal bidding strategy's structure. Further, an online receding optimization process is designed based on open-loop feedback control, which periodically updates a constructed optimal bid formulation that can be solved by linear programming. Then, considering large-scale linear programming problems, we propose an efficient down sampling scheme. Besides, a neural-network-based auction scale prediction is used to adapt to the changing market. Finally, a series of online A/B experiments onTaobao Sponsored Searchcompare our work to industrial methods and state-of-the-art from several aspects. The proposed method has been implemented onTaobao, a billion-scaled online advertising business, for over a year.

Click-through rate prediction aims to predict the ratio of clicks to impressions of a specific link, which is challenging due to (1) extremely high-dimensional categorical features; (2) both important original features and their interactions; and (3) reliance on different features and interactions in different time periods. To overcome these difficulties, we propose a new feature interaction detection method based on the idea of frequent itemset mining, named Online Random Intersection Chains (ORIC), which detects informative feature interactions with high interpretability. ORIC can be updated by controlling the importance of the historical and latest data with a tuning parameter, which saves computational burden and makes full use of historical information. Further, Streaming Integrated Model (SIM) is developed to feed the time-varying feature interactions into CTR prediction models. Empirical results on three benchmark datasets show that SIM achieves better performance than many CTR prediction models, as well as the efficiency, consistency, and interpretability of ORIC.

Predicting the probability that a user clicks a specific item is fundamental in online advertising and recommendation. Further, it is crucial to use the latest and historical data appropriately in online scenarios to train CTR models. Online Random Interaction Chains (ORIC) was proposed to detect informative and interpretable feature interactions without retraining on historical data in online scenario, and the Streaming Integrated Model (SIM) framework was designed to integrate these time-varying feature interactions into CTR prediction models. Unfortunately, ORIC exhibits latency when provides the feature interactions used to evaluate SIM, and ORIC is not applicable for numerical features. For these reasons, we propose ORIC-V2 that uses time series models to predict the confidence of candidate evaluating feature interactions and selects reasonable feature interactions, and combines numerical features with ORIC-V2 through a discretization model to obtain DORIC-V2. Feeding the feature interactions found by ORIC-V2 and DORIC-V2 into SIM obtains significant experimental results on three datasets, demonstrating the effectiveness and interpretability of ORIC-V2 and DORIC-V2.

With the growing size of datasets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are discrete and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves. Drawing inspiration from association rule mining, we come up with a method that uses association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computation complexity of the proposed algorithm to show its efficiency. And the results of a series of experiments verify the effectiveness of the algorithm.