DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide \& Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Corpus of public writing and its interest for the history of spanish: votive paintings of the province of Guadalajara

El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.The objective of this work is to present a special corpus of popular public writing in the centres of devotion of the province of Guadalajara; in particular a corpus of painted votive offering dating from the 17th to the 20th centuries. The pictures represent the event that gave rise to the miracle or favour received with his card; but in our corpus are only included those votive offerings that are accompanied by explanatory text, which testifies to the favour received with precise information, as name, cause, date, place, etc.The Corpus includes the reproduction of the votive offering, the paleographic transcription of the document and its critical presentation. The careful study of these three elements will allow us to extract interesting conclusions for the history of Spanish in aspects related to spelling, punctuation, syntactic structures and formulas used, lexicon, etc.; some samples that are collected in this article

PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network

The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the economies of scale, today's PCB design and manufacturing are often performed by suppliers around the globe, exposing them to many security vulnerabilities along the segmented PCB supply chain. Moreover, the increasing complexity of the PCB designs also leaves ample room for numerous sneaky board-level attacks to be implemented throughout each stage of a PCB's lifetime, threatening many electronic devices. In this paper, we propose PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework that can identify a wide spectrum of board-level malicious modifications. PDNPulse leverages the fact that the PDN's characteristics are inevitably affected by modifications to the PCB, no matter how minuscule. By detecting changes to the PDN impedance profile and using the Frechet distance-based anomaly detection algorithms, PDNPulse can robustly and successfully discern malicious modifications across the system. Using PDNPulse, we conduct extensive experiments on seven commercial-off-the-shelf PCBs, covering different design scales, different threat models, and seven different anomaly types. The results confirm that PDNPulse creates an effective security asymmetry between attack and defense

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and system design in DRS. Finally, we discuss appealing research directions and summarize the survey