Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, 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 and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

Design and Development of an Intelligent Online Personal Assistant in Social Learning Management Systems

Indiana University-Purdue University Indianapolis (IUPUI)Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Optimizing search user interfaces and interactions within professional social networks

Professional social networks (PSNs) play the key role in the online social media ecosystem, generate hundreds of terabytes of new data per day, and connect millions of people. To help users cope with the scale and influx of new information, PSNs provide search functionality. However, most of the search engines within PSNs today still provide only keyword queries, basic faceted search capabilities, and uninformative query-biased snippets overlooking the structured and interlinked nature of PSN entities. This results in siloed information, inefficient results presentation, and suboptimal search user experience (UX). In this thesis, we reconsider and comprehensively study input, control, and presentation elements of the search user interface (SUI) to enable more effective and efficient search within PSNs. Specifically, we demonstrate that: (1) named entity queries (NEQs) and structured queries (SQs) complement each other helping PSN users search for people and explore the PSN social graph beyond the first degree; (2) relevance-aware filtering saves users' efforts when they sort jobs, status updates, and people by an attribute value rather than by relevance; (3) extended informative structured snippets increase job search effectiveness and efficiency by leveraging human intelligence and exposing the most critical information about jobs right on a search engine result page (SERP); and (4) non-redundant delta snippets, which different from traditional query-biased snippets show on a SERP information relevant but complementary to the query, are more favored by users performing entity (e.g. people) search, lead to faster task completion times and better search outcomes. Thus, by modeling the structured and interlinked nature of PSN entities, we can optimize the query-refine-view interaction loop, facilitate serendipitous network exploration, and increase search utility. We believe that the insights, algorithms, and recommendations presented in this thesis will serve the next generation designers of SUIs within and beyond PSNs and shape the (structured) search landscape of the future

Human Resources Recommender system based on discrete variables

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing and Understanding has become one of the most exciting and challenging fields in the area of Artificial Intelligence and Machine Learning. With the rapidly changing business environment and surroundings, the importance of having the data transformed in such a way that makes it easy to interpret is the greatest competitive advantage a company can have. Having said this, the purpose of this thesis dissertation is to implement a recommender system for the Human Resources department in a company that will aid the decision-making process of filling a specific job position with the right candidate. The recommender system fill be fed with applicants, each being represented by their skills, and will produce a subset of most adequate candidates given a job position. This work uses StarSpace, a novelty neural embedding model, whose aim is to represent entities in a common vectorial space and further perform similarity measures amongst them

AI based e-recruitment system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonModern web-based e-recruitment methods have revolutionised advertising, source tracking, and online inquiry forms with the associated start-up and maintenance costs. Attracting and hiring qualified candidates, navigating online recruiting tools, increasing unsuitable applications, and discrimination and diversity issues are just a few of the drawbacks of e recruitment. A platform with AI algorithms is developed to overcome limitations, especially for Saudi private and public sector recruiters who lack AI in their application processes. The Unified Theory of Acceptance and Use of Technology (UTAT) measured user acceptance of e-recruitment systems, with a Cronbach's alpha of 0.96 indicating high reliability. The platform and its features were evaluated using five-point Likert scales, with mean responses exceeding 3.4, indicating high acceptability. This PhD developed the Artificial Intelligent Recruitment (AIRec) platform, ranking candidates with 99 per cent accuracy. Improve corporate image and profile, reduce recruitment and overhead costs, use better tools to select candidates based on sound criteria, provide tracking for both candidates and employers. AIRec also aims to change HR and line management culture and behaviour. The platform and its contributions were tested in real-world scenarios in the top Saudi government and university recruiting bodies. Based on Cronbach's alpha testing and validation, the result was 0.97 out of 1. The results show the system's high reliability

The Role of Informal Workers in Online Economic Crime

(Context) Online economic crime leverages information technologies (IT) for illegal wealth redistribution, such as banking theft. Such crime requires a series of actions, a scheme, to be successful. Informal workers, individuals whose economic activities escape regulations, can be leveraged to execute various tasks surrounding these schemes. However, what these workers represent for online economic crime organizations, and their impact on the reach and sophistication of the crime, has yet to be uncovered. This thesis focuses on understanding the contexts, motivations, and organizations of those behind online economic crime. While doing so, it assesses the role and availability of an informal IT workforce surrounding the crime organization and its likelihood to participate in such criminal schemes. (Methods and Data) This thesis builds on three data sources: (1) 21 semi-structured interviews with experts, (2) a private chat log containing discussions among individuals involved in online economic crime, and (3) two datasets on an informal IT workforce operating on a digital labor platform. A blend of qualitative and quantitative analyses is developed, including inductive thematic analysis, non-parametric statistical hypothesis tests, and group-based trajectory modeling. (Results) The findings illustrate three key contextual factors influencing those behind online economic crime: a lack of legal economic opportunities, a lack of deterrents and the availability of drifting means. Organizations behind online economic crime are found to take various forms, from organized, to enterprise-like, loose networks or communities. They are also characterized by a large sphere of influence given the indispensable workers hired to help with the crime orchestration. Among them, informal workers from the IT sector are found to be particularly important: they represent a pool of potential workers for all legal tasks surrounding online economic crime, and they can be leveraged easily due to digital labor platforms. However, further investigations illustrate that the benefits of hiring informal IT workers may be hindered by high transaction costs, including high hiring, switching, and monitoring costs. Moreover, the likelihood of informal IT workers to participate in crime-oriented spaces is found to be limited. (Conclusion) This study sheds light on the organization of online economic crime and the role of informal IT workers at the periphery. It provides both theoretical and empirical explanations as to why online economic crime is characterized by long reach, in terms of victims, and sophistication. It also offers nuanced concepts (e.g., drifters, informal workforce) to better grasp the organization of online economic crime and the degrees of involvement of those surrounding the crime