Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.Comment: 8 page

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

Nutrigenomics: future for sustenance

Nutrigenomics deals with the effect of foods and food constituents on gene expression. It is a new concept in disease prevention and cure. Nutrigenomics conveys how nutrients influence our body to express genes, whereas nutrigenetics refers to how our body responds to nutrients. The various bioactive food components can alter the gene expression mechanisms. But our actual knowledge is so insufficient that the only use of such information may help to satisfy our imagination. If science could arrive at some more precise facts, that would have vast applications in medicine

Modeling Overlapping Communities with Node Popularities

We develop a probabilistic approach for accurate network modeling using node popularities within the framework of the mixed-membership stochastic blockmodel (MMSB). Our model integrates two basic properties of nodes in social networks: homophily and preferential connection to popular nodes. We develop a scalable algorithm for posterior inference, based on a novel nonconjugate variant of stochastic variational inference. We evaluate the link prediction accuracy of our algorithm on nine real-world networks with up to 60,000 nodes, and on simulated networks with degree distributions that follow a power law. We demonstrate that the AMP predicts significantly better than the MMSB

Automated Design of Tailored Link Prediction Heuristics for Applications in Enterprise Network Security

The link prediction problem, which involves determining the likelihood of a relationship between objects, has numerous applications in the areas of recommendation systems, social networking, anomaly detection, and others. A variety of link prediction techniques have been developed to improve predictive performance for different application domains. Selection of the appropriate link prediction heuristic is critical which demonstrates the need for tailored solutions. This work explores the use of hyper-heuristics to automate the selection and generation of customized link prediction algorithms. A genetic programming approach is used to evolve novel solutions from functionality present in existing techniques that exploit characteristics of a specific application to improve performance. Applications of this approach are tested using data from a real-world enterprise computer network to differentiate normal activity from randomly generated anomalous events. Results are presented that demonstrate the potential for the automated design of custom link prediction heuristics that improve upon the predictive capabilities of conventional methods

Scaling probabilistic models of genetic variation to millions of humans

A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. The aggregated number of genotyped humans is currently on the order millions of individuals, and existing methods do not scale to data of this size. To solve this problem we developed TeraStructure, an algorithm to fit Bayesian models of genetic variation in structured human populations on tera-sample-sized data sets (10(12) observed genotypes, e.g., 1M individuals at 1M SNPs). TeraStructure is a scalable approach to Bayesian inference in which subsamples of markers are used to update an estimate of the latent population structure between samples. We demonstrate that TeraStructure performs as well as existing methods on current globally sampled data, and we show using simulations that TeraStructure continues to be accurate and is the only method that can scale to tera-sample-sizes