Recurrent Poisson Factorization for Temporal Recommendation

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes are available at https://github.com/AHosseini/RP

Thermal Fatigue Behavior of Asphalt Concrete: A Laboratory-based Investigation Approach

Thermal fatigue leads to serious degradations of structural performance and service quality of roadways. Thermal fatigue cracks occur in moderate climates, and are the result of generated cyclic thermal strains/stresses within the restrained pavement layers. In this study, an experimental setup is developed to measure the thermal fatigue resistance of asphalt concrete specimens under constant strain amplitude loading. To simulate thermal fatigue behavior of asphalt concrete, uniaxial loading is mechanically applied to achieve constant amplitude sinusoidal strains at a frequency of 0.01Hz. The results of statistical analyses indicate the asphalt content, aggregate source and asphalt binder type have the strongest effect on the thermal fatigue resistance of asphalt concrete

Development of Carbon Fiber-modified Electrically Conductive Concrete for Implementation in Des Moines International Airport

This paper reports on the procedures of mix design preparation, production, placement, and performance evaluation of the first electrically conductive concrete (ECON) heated-pavement system (HPS) implemented at a U.S. airport. While ECON has drawn considerable attention as a paving material for multi-functional pavements, including HPS, the majority of ECON HPS applications and studies have been limited to laboratory scale or include materials/methods that do not conform to regulations enforced by airfield construction practices. Carbon fiber-reinforced ECON provides a promising prospective for application in airfield pavements. In this study, ECON mixtures were prepared in the laboratory using varying cementitious materials, aggregate systems, water-to-cementitious ratios, carbon fiber dosages, and admixtures. The results of tests on laboratory-prepared mixes were utilized to find the most suitable ECON mix design for application in an HPS test section at the Des Moines International Airport. The properties of the ECON produced at the concrete plant were measured and compared with equivalent laboratory-prepared samples. The final mix design exhibited electrical resistivity of 115 Ω-cm in the laboratory and 992 Ω-cm in the field, while completely meeting strength and workability requirements. Despite the higher ECON resistivity obtained in large-scale production, the fabricated HPS exhibited desirable performance with respect to deicing and anti-icing operations. The test section was able to generate a 300–350 W/m2 power density and to effectively melt ice/snow with this level of energy

Influence of mix design variables on engineering properties of carbon fiber-modified electrically conductive concrete

This research was inspired by the need to optimize the mix design of electrically conductive concrete (ECON) for field implementation. Carbon fiber was used for producing ECON with different mixing proportions and constituents. Calcium nitrite-based corrosion inhibitor admixture and methylcellulose were used as conductivity-enhancing agent (CEA) and fiber-dispersive agent (FDA) respectively. Five easy-to-change mix design variables were evaluated for their effects on electrical conductivity and strength of ECON: carbon fiber dosage, fiber length, coarse-to-fine aggregate volume ratio (C/F), CEA dosage, and FDA dosage. The results approved the effectiveness of the applied CEA in improving electrical conductivity while positively influencing strength. Conductivity was significantly influenced by: fiber content, C/F, fiber length, and CEA dosage. The dosages of Fiber, CEA, and FDA exerted significant influence on compressive strength. C/F and FDA dosage were significant variables influencing flexural strength