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In the recruitment domain, knowing the employer industry of jobs is important to get an insight about the demand in each industry. The existing system at CareerBuilder uses an employer name normalization system and an employer knowledge base to infer the employer industry of a job. However, errors may occur during the computation of the job employer and in the construction of the employer knowledge...
According to a report online [34], more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed, Dice and CareerBuilder. Recently, Google released their “AIpowered Jobs Search Engine”, “Google For Jobs”...
Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user...
Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries,...
The worldwide market for luxury and fashion goods is dominated today by a handful of multinational corporations (MNCs). The way MNCs access foreign markets and organize distribution, however, remains unclear. In this paper, based on an analysis of foreign trade statistics, we take the example of watches and provide a model to highlight the most important flows as well as regional hubs in this global...
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at...
Automatic creation of polarity dictionaries is an important issue, as explanations of prediction models are often required in the financial industry. This paper proposes a novel method of developing an interpretable and predictable neural network model. The neural network model we built can extract polarity scores of concepts from documents. Furthermore, we can detect pairwise interactions between...
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