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Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction...
There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection...
Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non–stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large–scale real–world...
Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a...
Graphs or networks are a natural way to analyze inter-related set of entities. When these entities are associated with a diverse number of features, each denoting a specific perspective, then the representation can be simplified by forming a network of layers (one for each feature) or multiplexes. Vertices with high centrality values in the multiplexes represent the most influential vertices. However,...
Multidimensional relationships can be represented as a multi-mode network or graph, where each vertex or node corresponds to an object, and each edge or link is attributed to one of the multiple types of relationships between a pair of objects. Web search log includes users' search behavior and can also be represented as such a multi-mode network, where each vertex corresponds to a query and each...
The interests of individual Internet users fall into a hierarchical structure which is useful in regards to building personalized searches and recommendations. Most studies on this subject construct the interest hierarchy of a single person from the document perspective. In this study, we constructed the user interest hierarchy via user profiles. We organized 433,397 user interests, referred to here...
Network centrality reflects node importance in networks, which is a challenging problem in social network analysis. Based on Fuzzy Set and MYCIN theory, this paper proposes a novel node centrality measuring method and models n-monkeys dataset, where n is 20. Initially, we created monkeys relationship graph and generated relationship matrix based on the monkeys' encountering times in a specific time...
In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large...
Job ad data has become an essential part of the recruiting world, helping recruiters to construct views of the labor market to determine emerging skills, closest competitors, and where to get the most value for each recruiting dollar spent. Collecting this data, however, can be problematic, as job ads are posted redundantly at numerous online locations. In this paper, we detail a domain-specific near-duplicate...
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...
Finding the best candidates to match a set of job requirements can be viewed as both an art and a science. In this paper, we conduct an empirical study using actual job candidates and job applicants. We compare the ranked lists generated by executive recruiting experts with the list generated by three search strategies: one using crowdworkers in a gamified environment, a second using information retrieval-based...
Traditionally, the time-to-fill metric is used as a scorecard for past performance. An organization may use time to fill to assess the performance of its internal recruiting team, or as a way to set service level agreements with outsourced recruiting partners. By first developing a set of quantifiable job features and then applying survival analysis to historical time-to-fill data, we build a predictor...
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...
Online job boards are used by millions of job seekers, who browse through the postings for jobs that match their interest. Queries are crafted using terminology generated by the users, which may not match the language used in the job postings. Semantic enrichment methods attempt to fill such a lexical gap by re-writing the queries based on richer terms, which are mined using behavioral logs. However,...
Continuous training is crucial for creating and maintaining the right skill-profile for the industrial organization's workforce. There is a tremendous variety in the available trainings within an organization: technical, project management, quality, leadership, domain-specific, soft-skills etc. Hence it is important to assist the employee in choosing the best trainings, which perfectly suits her background,...
This paper proposes an approach to estimating fungibility between skills given multiple information sources of those skills. An estimate of skill adjacency or fungibility or substitutability is critical for effective capacity planning, analytics and optimization in the face of changing skill requirements of an organization. The proposed approach is based on computing a similarity measure between skills,...
Candidates routinely use a set of key phrases or keywords to succinctly describe their expertise or skillset. This is useful for both matching candidate profiles to jobs and for comparing different candidates. Constant development of businesses and labour market has dynamic impact on importance of such skills, where importance of each skill may evolve with time. At any given time, some skills may...
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