Click-through rate (CTR) prediction is a critical task in online advertising systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They consider each target ad independently and cannot directly extract useful information contained in users' historical ad impressions and clicks. In contrast, models like Recurrent Neural Networks (RNNs) are stateful but complex. They model temporal dependency between users' sequential behaviors and can achieve improved prediction performance than DNNs. However, both the offline training and online prediction process of RNNs are much more complex and time-consuming. In this paper, we propose Memory Augmented DNN (MA-DNN) for practical CTR prediction services. In particular, we create two external memory vectors for each user, memorizing high-level abstractions of what a user possibly likes and dislikes. The proposed MA-DNN achieves a good compromise between DNN and RNN. It is as simple as DNN, but has certain ability to exploit useful information contained in users' historical behaviors as RNN. Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services. Actually, the memory component can be augmented to other models as well (e.g., the Wide&Deep model).
In this paper, we propose Memory Augmented Deep Neural Network (MA-DNN) for CTR prediction. The proposed MA-DNN achieves a good compromise between DNN and RNN. We are aware of recent work like @cite_12 that also utilizes memory networks @cite_17 . However, @cite_12 is proposed for recommender systems and our way of designing the user memory component is different from that in @cite_12 .
@cite_12 @cite_17
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Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serous threat to their applications in security-sensitive systems. In this paper, we propose a simple yet effective defense algorithm Defense-VAE that uses variational autoencoder (VAE) to purge adversarial perturbations from contaminated images. The proposed method is generic and can defend white-box and black-box attacks without the need of retraining the original CNN classifiers, and can further strengthen the defense by retraining CNN or end-to-end finetuning the whole pipeline. In addition, the proposed method is very efficient compared to the optimization-based alternatives, such as Defense-GAN, since no iterative optimization is needed for online prediction. Extensive experiments on MNIST, Fashion-MNIST, CelebA and CIFAR-10 demonstrate the superior defense accuracy of Defense-VAE compared to Defense-GAN, while being 50x faster than the latter. This makes Defense-VAE widely deployable in real-time security-sensitive systems. We plan to open source our implementation to facilitate the research in this area. Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed. However, although they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby limiting their wide adoption. The energy consumption of DNNs is driven by both memory accesses and computation. Binarized Neural Networks (BNNs), as a trade-off between accuracy and energy consumption, can achieve great energy reduction, and have good accuracy for large DNNs due to its regularization effect. However, BNNs show poor accuracy when a smaller DNN configuration is adopted. In this paper, we propose a new DNN model, LightNN, which replaces the multiplications to one shift or a constrained number of shifts and adds. For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs. Therefore, LightNNs provide more options for hardware designers to make trade-offs between accuracy and energy. Moreover, for large DNN configurations, LightNNs have a regularization effect, making them better in accuracy than conventional DNNs. These conclusions are verified by experiment using the MNIST and CIFAR-10 datasets for different DNN configurations. Recently proposed deep neural network (DNN) obtains significant accuracy improvements in many large vocabulary continuous speech recognition (LVCSR) tasks. However, DNN requires much more parameters than traditional systems, which brings huge cost during online evaluation, and also limits the application of DNN in a lot of scenarios. In this paper we present our new effort on DNN aiming at reducing the model size while keeping the accuracy improvements. We apply singular value decomposition (SVD) on the weight matrices in DNN, and then restructure the model based on the inherent sparseness of the original matrices. After restructuring we can reduce the DNN model size significantly with negligible accuracy loss. We also fine-tune the restructured model using the regular back-propagation method to get the accuracy back when reducing the DNN model size heavily. The proposed method has been evaluated on two LVCSR tasks, with context-dependent DNN hidden Markov model (CD-DNN-HMM). Experimental results show that the proposed approach dramatically reduces the DNN model size by more than 80 without losing any accuracy. Index Terms: deep neural network, singular value decomposition, model restructuring In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising. We use RNNs to map both queries and ads to real valued vectors, with which the relevance of a given (query, ad) pair can be easily computed. On top of the RNN, we propose a novel attention network, which learns to assign attention scores to different word locations according to their intent importance (hence the name DeepIntent). The vector output of a sequence is thus computed by a weighted sum of the hidden states of the RNN at each word according their attention scores. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs, which are sampled from a commercial search engine. We show that in most cases the attention network improves the quality of learned vector representations, evaluated by AUC on a manually labeled dataset. Moreover, we highlight the effectiveness of the learned attention scores from two aspects: query rewriting and a modified BM25 metric. We show that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods. Also, by modifying the term frequency with the attention scores in a standard BM25 formula, one is able to improve its performance evaluated by AUC.