In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4 in offline experiments and up to 17.5 in an online A B test on a commercial RTB platform over the widely applied bidding strategies.
Linear models are simple and effective in learning, but may fail to capture the interactions between the assumed (conditionally) independent raw features @cite_12 . By contrast, non-linear models are capable of learning feature interactions in various ways and could potentially improve prediction performance @cite_26 @cite_13 . Gradient boosting decision trees (GBDT) @cite_13 @cite_18 are a straightforward non-linear model to capture feature interactions. Moreover, latent factor models, particularly factorization machines (FMs) @cite_26 , map each binary feature into a low dimensional continuous space, and the feature interaction is automatically explored via vector inner product.
@cite_18 @cite_26 @cite_13 @cite_12
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Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features. Factorization Machines (FMs) are a popular solution for efficiently using the second-order feature interactions. However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data. While deep neural networks have recently been applied to learn non-linear feature interactions in industry, such as the Wide&Deep by Google and DeepCross by Microsoft, the deep structure meanwhile makes them difficult to train. In this paper, we propose a novel model Neural Factorization Machine (NFM) for prediction under sparse settings. NFM seamlessly combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions. Conceptually, NFM is more expressive than FM since FM can be seen as a special case of NFM without hidden layers. Empirical results on two regression tasks show that with one hidden layer only, NFM significantly outperforms FM with a 7.3 relative improvement. Compared to the recent deep learning methods Wide&Deep and DeepCross, our NFM uses a shallower structure but offers better performance, being much easier to train and tune in practice. This paper combines linear spun coding and nonnegative matrix factorization into sparse non-negative matrix factorization. In contrast to non-negative matrix factorization, the new model can learn much sparser representation via imposing sparseness constraints explicitly; in contrast to a close model -non-negative sparse coding, the new model can learn parts-based representation via fully multiplicative updates because of adapting a generalized Kullback-Leibler divergence instead of the conventional mean error for approximation error. Experiments on MIT-CBCL training facts data demonstrate the effectiveness of the proposed method. The most common approach in predictive modeling is to describe cases with feature vectors (aka design matrix). Many machine learning methods such as linear regression or support vector machines rely on this representation. However, when the underlying data has strong relational patterns, especially relations with high cardinality, the design matrix can get very large which can make learning and prediction slow or even infeasible. This work solves this issue by making use of repeating patterns in the design matrix which stem from the underlying relational structure of the data. It is shown how coordinate descent learning and Bayesian Markov Chain Monte Carlo inference can be scaled for linear regression and factorization machine models. Empirically, it is shown on two large scale and very competitive datasets (Netflix prize, KDDCup 2012), that (1) standard learning algorithms based on the design matrix representation cannot scale to relational predictor variables, (2) the proposed new algorithms scale and (3) the predictive quality of the proposed generic feature-based approach is as good as the best specialized models that have been tailored to the respective tasks. Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient training with several loss functions, the problem of endowing learners with a mechanism for feature selection is still unsolved. Common approaches employ ad hoc filtering or L1-regularization; both ignore the structure of the feature space, preventing practicioners from encoding structural prior knowledge. We fill this gap by adopting regularizers that promote structured sparsity, along with efficient algorithms to handle them. Experiments on three tasks (chunking, entity recognition, and dependency parsing) show gains in performance, compactness, and model interpretability.