Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.Comment: WSDM 201

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Continuous heating of a giant X-ray flare on Algol

Giant flares can release large amounts of energy within a few days: X-ray emission alone can be up to ten percent of the star's bolometric luminosity. These flares exceed the luminosities of the largest solar flares by many orders of magnitude, which suggests that the underlying physical mechanisms supplying the energy are different from those on the Sun. Magnetic coupling between the components in a binary system or between a young star and an accretion disk has been proposed as a prerequisite for giant flares. Here we report X-ray observations of a giant flare on Algol B, a giant star in an eclipsing binary system. We observed a total X-ray eclipse of the flare, which demonstrates that the plasma was confined to Algol B, and reached a maximum height of 0.6 stellar radii above its surface. The flare occurred around the south pole of Algol B, and energy must have been released continously throughout its life. We conclude that a specific extrastellar environment is not required for the presence of a flare, and that the processes at work are therefore similar to those on the Sun.Comment: Nature, Sept. 2 199

On Sampling Strategies for Neural Network-based Collaborative Filtering

Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.Comment: This is a longer version (with supplementary attached) of the KDD'17 pape

A Search for the Near-Infrared Counterpart to GCRT J1745-3009

We present an optical/near-infrared search for a counterpart to the perplexing radio transient GCRT J1745-3009, a source located ~1 degree from the Galactic Center. Motivated by some similarities to radio bursts from nearby ultracool dwarfs, and by a distance upper limit of 70 pc for the emission to not violate the 1e12 K brightness temperature limit for incoherent radiation, we searched for a nearby star at the position of GCRT J1745-3009. We found only a single marginal candidate, limiting the presence of any late-type star to >1 kpc (spectral types earlier than M9), >200 pc (spectral types L and T0-T4), and >100 pc (spectral types T4-T7), thus severely restricting the possible local counterparts to GCRT J1745-3009. We also exclude any white dwarf within 1 kpc or a supergiant star out to the distance of the Galactic Center as possible counterparts. This implies that GCRT J1745-3009 likely requires a coherent emission process, although whether or not it reflects a new class of sources is unclear.Comment: 10 pages, 5 figures. Accepted for publication in the Astrophysical Journa

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

Detection of hard X-rays from a Class I protostar in the HH24-26 region in the Orion Molecular Cloud

We observed the HH24-26 region in the L1630 Orion molecular cloud complex with the X-ray observatory ASCA in the 0.5$-$10 keV band. X-ray emission was detected from the T Tauri star SSV61 and from the region where the Class I protostars SSV63E and SSV63W are located (hereafter SSV63E+W). The spectra of both SSV63E+W and SSV61 are well explained by an optically thin thermal plasma model. The spectrum of the T Tauri star SSV61 has a low temperature of $kT=0.9$ (0.7$-$1.2) keV and a moderate absorption of $N_{\rm{H}}=1.3$ (0.9$-$1.7) $\times10^{22}$ cm$^{-2}$, while that of the protostar SSV63E+W has a high temperature of $kT=5.0$ (3.3$-$7.9) keV and a heavy absorption of $N_{\rm{H}}=1.5$ (1.2$-$1.8) $\times10^{23}$ cm$^{-2}$. The X-ray light curve of SSV63E+W showed a flare during the observation. The peak flux reached about 9 times that of the quiescent flux. The temperature and the absorption column density do not change conspicuously during the flare. The 0.5$-$10 keV luminosity of SSV63E+W was about $1\times10^{32}$ erg s$^{-1}$ in the quiescent state. The present detection of hard X-rays from SSV63E+W is remarkable, because this is the first X-ray detection of a protostar in Orion.Comment: 14 pages, 3 postscript figures, uses aasms4.st

Quasi-periodic X-ray Flares from the Protostar YLW15

With ASCA, we have detected three X-ray flares from the Class I protostar YLW15. The flares occurred every ~20 hours and showed an exponential decay with time constant 30-60 ks. The X-ray spectra are explained by a thin thermal plasma emission. The plasma temperature shows a fast-rise and slow-decay for each flare with kT_{peak}~4-6 keV. The emission measure of the plasma shows this time profile only for the first flare, and remains almost constant during the second and third flares at the level of the tail of the first flare. The peak flare luminosities L_{X,peak} were ~5-20 * 10^{31} erg s^{-1}, which are among the brightest X-ray luminosities observed to date for Class I protostars. The total energy released in each flare was 3-6*10^{36} ergs. The first flare is well reproduced by the quasi-static cooling model, which is based on solar flares, and it suggests that the plasma cools mainly radiatively, confined by a semi-circular magnetic loop of length ~14 Ro with diameter-to-length ratio \~0.07. The two subsequent flares were consistent with the reheating of the same magnetic structure as of the first flare. The large-scale magnetic structure and the periodicity of the flares imply that the reheating events of the same magnetic loop originate in an interaction between the star and the disk due to the differential rotation.Comment: Accepted by ApJ, 9 pages incl. 4 ps figure

Rotation and X-ray emission from protostars

The ASCA satellite has recently detected variable hard X-ray emission from two Class I protostars in the rho Oph cloud, YLW15 (IRS43) and WL6, with a characteristic time scale ~20h. In YLW15, the X-ray emission is in the form of quasi-periodic energetic flares, which we explain in terms of strong magnetic shearing and reconnection between the central star and the accretion disk. In WL6, X-ray flaring is rotationally modulated, and appears to be more like the solar-type magnetic activity ubiquitous on T Tauri stars. We find that YLW15 is a fast rotator (near break-up), while WL6 rotates with a significantly longer period. We derive a mass M_\star ~ 2 M_\odot and \simlt 0.4 M_\odot for the central stars of YLW15 and WL6 respectively. On the long term, the interactions between the star and the disk results in magnetic braking and angular momentum loss of the star. On time scales t_{br} ~ a few 10^5 yrs, i.e., of the same order as the estimated duration of the Class~I protostar stage. Close to the birthline there must be a mass-rotation relation, t_{br} \simpropto M_\star, such that stars with M_\star \simgt 1-2 M_\odot are fast rotators, while their lower-mass counterparts have had the time to spin down. The rapid rotation and strong star-disk magnetic interactions of YLW15 also naturally explain the observation of X-ray ``superflares''. In the case of YLW15, and perhaps also of other protostars, a hot coronal wind (T~10^6 K) may be responsible for the VLA thermal radio emission. This paper thus proposes the first clues to the rotation status and evolution of protostars.Comment: 13 pages with 6 figures. To be published in ApJ (April 10, 2000 Part 1 issue