Network Embeddedness and the Exploration of Novel Technologies: Technological Distance, Betweenness Centrality and Density

In this paper we analyze the innovative performance of alliance networks as a function of the technological distance between partners, a firm's network position (centrality) and total network density.We study how these three elements of an alliance network, apart and in combination, affect the 'twin tasks' in exploration, namely novelty creation on the one hand and its efficient absorption on the other hand.For an empirical test, we study technology-based alliance networks in the pharmaceutical, chemical and automotive industry.innovation networks;cognitive distance;centrality;density

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

Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that the features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems (RecSys 18

Filaggrin gene defects and risk of developing allergic sensitisation and allergic disorders: systematic review and meta-analysis

Objective To investigate whether filaggrin gene defects, present in up to one in 10 western Europeans and North Americans, increase the risk of developing allergic sensitisation and allergic disorders

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

The Patterns of High-Level Magnetic Activity Occurring on the Surface of V1285 Aql: The OPEA Model of Flares and DFT Models of Stellar Spots

Statistically analyzing Johnson UBVR observations of V1285 Aql during the three observing seasons, both activity level and behavior of the star are discussed in respect to obtained results. We also discuss the out-of-flare variation due to rotational modulation. Eighty-three flares were detected in the U-band observations of season 2006 . First, depending on statistical analyses using the independent samples t-test, the flares were divided into two classes as the fast and the slow flares. According to the results of the test, there is a difference of about 73 s between the flare-equivalent durations of slow and fast flares. The difference should be the difference mentioned in the theoretical models. Second, using the one-phase exponential association function, the distribution of the flare-equivalent durations versus the flare total durations was modeled. Analyzing the model, some parameters such as plateau, half-life values, mean average of the flare-equivalent durations, maximum flare rise, and total duration times are derived. The plateau value, which is an indicator of the saturation level of white-light flares, was derived as 2.421{\pm}0.058 s in this model, while half-life is computed as 201 s. Analyses showed that observed maximum value of flare total duration is 4641 s, while observed maximum flare rise time is 1817 s. According to these results, although computed energies of the flares occurring on the surface of V1285 Aql are generally lower than those of other stars, the length of its flaring loop can be higher than those of more active stars.Comment: 44 pages, 10 figures, 5 tables, 2011PASP..123..659