4,618 research outputs found
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
From Text to Knowledge with Graphs: modelling, querying and exploiting textual content
This paper highlights the challenges, current trends, and open issues related
to the representation, querying and analytics of content extracted from texts.
The internet contains vast text-based information on various subjects,
including commercial documents, medical records, scientific experiments,
engineering tests, and events that impact urban and natural environments.
Extracting knowledge from this text involves understanding the nuances of
natural language and accurately representing the content without losing
information. This allows knowledge to be accessed, inferred, or discovered. To
achieve this, combining results from various fields, such as linguistics,
natural language processing, knowledge representation, data storage, querying,
and analytics, is necessary. The vision in this paper is that graphs can be a
well-suited text content representation once annotated and the right querying
and analytics techniques are applied. This paper discusses this hypothesis from
the perspective of linguistics, natural language processing, graph models and
databases and artificial intelligence provided by the panellists of the DOING
session in the MADICS Symposium 2022
Deep Learning-Based User Feedback Classification in Mobile App Reviews
As online users are interacting with many mobile apps under different usage contexts, user needs in an app design process have become a critical issue. Existing studies indicate timely and constructive online reviews from users become extremely crucial for developers to understand user needs and create innovation opportunities. However, discovering and quantifying potential user needs from large amounts of unstructured text is a nontrivial task. In this paper, we propose a domain-oriented deep learning approach that can discover the most critical user needs such as app product new features and bug reports from a large volume of online product reviews. We conduct comprehensive evaluations including quantitative evaluations like F-measure a, and qualitative evaluations such as a case study to ensure the quality of discovered information, specifically, including the number of bug reports and feature requests. Experimental results demonstrate that our proposed supervised model outperforms the baseline models and could find more valuable information such as more important keywords and more coherent topics. Our research has significant managerial implications for app developers, app customers, and app platform providers
Real-Time Purchase Prediction Using Retail Video Analytics
The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape
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