Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Pythia: AI-assisted Code Completion System

In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used by software developers at edit time. The system is currently deployed as part of Intellicode extension in Visual Studio Code IDE. Pythia exploits state-of-the-art large-scale deep learning models trained on code contexts extracted from abstract syntax trees. It is designed to work at a high throughput predicting the best matching code completions on the order of 100 $ms$. We describe the architecture of the system, perform comparisons to frequency-based approach and invocation-based Markov Chain language model, and discuss challenges serving Pythia models on lightweight client devices. The offline evaluation results obtained on 2700 Python open source software GitHub repositories show a top-5 accuracy of 92\%, surpassing the baseline models by 20\% averaged over classes, for both intra and cross-project settings.Comment: Published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19

Nonequilibrium spectral diffusion due to laser heating in stimulated photon echo spectroscopy of low temperature glasses

A quantitative theory is developed, which accounts for heating artifacts in three-pulse photon echo (3PE) experiments. The heat diffusion equation is solved and the average value of the temperature in the focal volume of the laser is determined as a function of the 3PE waiting time. This temperature is used in the framework of nonequilibrium spectral diffusion theory to calculate the effective homogeneous linewidth of an ensemble of probe molecules embedded in an amorphous host. The theory fits recently observed plateaus and bumps without introducing a gap in the distribution function of flip rates of the two-level systems or any other major modification of the standard tunneling model.Comment: 10 pages, Revtex, 6 eps-figures, accepted for publication in Phys. Rev.

DOE-NSF-NIH Workshop on Opportunities in THz Science, February 12-14, 2004

This is the report of the Workshop on Opportunities in THz Science, held on February 12-14, 2004 in Arlington, VA. This workshop brought together researchers who use or produce THz radiation for physics, chemistry, biology, medicine, and materials science to discuss new research opportunities and common resource needs. The charge from the sponsors of the workshop was to focus on basic science questions within these disciplines that have and can be answered using THz radiation

The social gradient in cultural consumption and the information-processing hypothesis

Patterns of cultural consumption have a strong social gradient which is primarily driven by education, but what explains these educational differences in cultural preferences remains unclear. Explanations based on information processing capacity have gained widespread currency; the perceived cognitive ‘difficulty’ of both appreciating high culture, and of maintaining broad, omnivorous tastes. If, on average, high culture is more complex than low culture then a higher level of information processing capacity may be required to derive enjoyment from it. In contrast, socialization theories suggest that exposure to ‘high’ culture, may explain this gradient, particularly among university graduates with degrees in the Arts or Humanities. To test these two theories we use the Cultural Capital and Social Exclusion survey (n = 1,079) and estimate the association between degree type and measures of cultural preference and consumption, including: film directors, artists, and cultural participation. Compared to non-graduates, arts, humanities, and social science graduates are more likely to enjoy highbrow directors and artists, and are more likely to be cultural omnivores; while graduates from other subjects are not clearly distinct from non-graduates in their cultural preferences. These findings suggest that information processing plays a minor role in shaping the social gradient in cultural consumption