Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions.Therefore,the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.Comment: 36 pages, 6 figure

Separation of core-shell structured carbon black nanoparticles from waste tires by light pyrolysis

The separation of core-shell structured carbon black (CBlp) nanoparticles from waste tires was investigated by applying a reactive extrusion process. The polymeric shell consisting primarily of crosslinked rubber and loosely bound rubber could be selectively separated by varying the extrusion temperature to 260, 280 and 300 °C. The structure, chemical composition and structure of the separated CBlp were characterized using thermo-gravimetric analysis, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy and dynamic light scattering. The crosslinked structure was persevered in the rubber shell of CBlp after extruding at 260 °C. A layer of loosely bound rubber was observed only in the rubber shell when extruded at 280 °C and 300 °C. The composition of the bound rubber layer is also dependent on the processing temperature

Core-shell structured carbon nanoparticles derived from light pyrolysis of waste tires

Carbon black nanoparticles (CBlp) were derived from waste tire rubbers via a melt-extrusion pyrolysis process at 300 °C. A polymeric shell was observed on the surface of CBlp, which was formed by bound rubber. The chemical structure and content of the bound rubber shell were characterized and quantified, and compared with the commercial carbon black N330 and pyrolytic carbon black (CBp). The average particle size of CBlp is about 22 nm, with a rubber shell thickness of 7–12 nm. Functional carboxylic group and ZnO were detected on the surface of CBlp by FTIR and XRD, respectively, which are absent from N330 and CBp. The core-shell structure of CBlp facilitate the dispersion and interfacial interaction in natural rubber, and lead to a higher reinforcement effect as compared those of N330 and CBp. The light pyrolysis process provides a facile and clean approach to generate useful carbon nanoparticles out of waste tire rubbers