scholarly journals Spectrum of the complex Laplacian on product domains

2010 ◽  
Vol 138 (09) ◽  
pp. 3187-3187 ◽  
Author(s):  
Debraj Chakrabarti
2020 ◽  
Vol 5 (2) ◽  
pp. 212
Author(s):  
Hamdi Ahmad Zuhri ◽  
Nur Ulfa Maulidevi

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.


Author(s):  
Xiaojing Lu ◽  
Ronald E. Goldsmith ◽  
Margherita Pagani

This chapter introduces the concept of “two-sided” markets and shows how they comprise a unique type of social media that facilitates the development of social networks oriented toward specific product domains (e.g., restaurants), specific brands (e.g., Starbucks), or common consumer concerns (e.g., Yelp.com). Not only do two-sided-markets constitute a unique type of Website, they can be integrated with or linked to other social media, thereby enriching the value of both the two-sided market and its partner(s). Because a two-sided market increases in value for all three parties that constitute it (consumers, the platform, and vendors) as the number of both vendors and consumer participants grows, platform managers are eager to use incentive strategies to encourage consumers to increase their active use of the site. Among these incentive strategies are various reward programs that stimulate use by rewarding consumers who add content, post reviews, comment on others’ reviews, and more. Part of this chapter describes two online experiments that demonstrate that two types of common reward programs, monetary and social rewards (Heyman & Ariely, 2004), are effective in stimulating consumer intent to use the site more actively than without a reward. Finally, we make several suggestions for integrating two-sided markets into other social media, and we propose several avenues for future research into this topic that should increase our understanding of how consumers behave in two-sided markets and how platform managers can both enhance active use and use the information derived from this use.


Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.


2016 ◽  
Vol 14 (1) ◽  
pp. 649-660 ◽  
Author(s):  
Mohammed Ali ◽  
Mohammed Al-Dolat

Abstract In this paper, we study the the parabolic Marcinkiewicz integral ${\cal M}_{\Omega, h}^{{\rho _{1,}}{\rho _2}}$ on product domains Rn × Rm (n, m ≥ 2). Lp estimates of such operators are obtained under weak conditions on the kernels. These estimates allow us to use an extrapolation argument to obtain some new and improved results on parabolic Marcinkiewicz integral operators.


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