Click-through rate (CTR) prediction is an important topic in mobile recommendation systems and computational advertising. As previous research indicates, a key point to maximize CTR is feature conjunction for making the training data more informative. Despite great progress, existing methods still fail to choose suitable settings of feature conjunction for the given data. In particular, a linear model on the pair-wise feature conjunction may overfit the training set if the data set is highly sparse. For such data, a model based on low-rank latent matrices are shown to be more appropriate. Unfortunately, practitioners now face difficulties to decide when to use which. In this paper, we propose an adaptive framework to address the feature conjunction problem. Our proposed framework adaptively chooses effective models to do feature conjunction according to data properties. We offer a case for building feature conjunction based on feature-pair frequency. Efficient training, as well as parameter selection, are thoroughly investigated. We conduct comprehensive online and offline experiments to demonstrate the effectiveness of the adaptive model over existing models for CTR prediction.

Recommender system is one of the most successful machine learning technologies for commerce. However, it can reinforce the closed feedback loop problem, where the recommender system generates items to users, then the further recommendation model is trained with the data that users' feedback to the items. Such self-reinforcing pattern can cause data bias problems. There are several debiasing methods, inverse-propensity-scoring (IPS) is a practical one for industry product. Since it is relatively easy to reweight training samples, and ameliorate the distribution shift problem. However,because of deterministic policy problem and confoundings in real-world data, it is hard to predict propensity score accurately. Inspired by the sample reweight work for robust deep learning, we propose a novel influence function based method for recommendation modeling, and analyze how the influence function corrects the bias. In the experiments, our proposed method achieves better performance against the state-of-the-art approaches.

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.