21,301 research outputs found
Reflecting on E-Recruiting Research Using Grounded Theory
This paper presents a systematic review of the e-Recruiting literature through a grounded theory lens. The large number of publications and the increasing diversity of publications on e-Recruiting research, as the most studied area within e-HRM (Electronic Human Resource Management), calls for a synthesis of e-Recruiting research. We show interconnections between achievements, research gaps and future research directions in order to advance both e-Recruiting research and practice. Moreover, we provide a definition of e-Recruiting. The use of grounded theory enabled us to reach across sub-disciplines, methods used, perspectives studied, themes discussed and stakeholders involved. We demonstrate that the Grounded Theory Approach led to a better understanding of the interconnections that lay buried in the disparate e-Recruiting literature
Automatic supervised information extraction of structured web data
The overall purpose of this project is, in short words, to create a system able to extract vital
information from product web pages just like a human would. Information like the name of the
product, its description, price tag, company that produces it, and so on. At a first glimpse, this
may not seem extraordinary or technically difficult, since web scraping techniques exist from long
ago (like the python library Beautiful Soup for instance, an HTML parser1 released in 2004). But
let us think for a second on what it actually means being able to extract desired information from
any given web source: the way information is displayed can be extremely varied, not only visually,
but also semantically. For instance, some hotel booking web pages display at once all prices for
the different room types, while medium-sized consumer products in websites like Amazon offer the
main product in detail and then more small-sized product recommendations further down the page,
being the latter the preferred way of displaying assets by most retail companies. And each with its
own styling and search engines. With the above said, the task of mining valuable data from the
web now does not sound as easy as it first seemed. Hence the purpose of this project is to shine
some light on the Automatic Supervised Information Extraction of Structured Web Data problem.
It is important to think if developing such a solution is really valuable at all. Such an endeavour
both in time and computing resources should lead to a useful end result, at least on paper, to
justify it. The opinion of this author is that it does lead to a potentially valuable result. The
targeted extraction of information of publicly available consumer-oriented content at large scale in
an accurate, reliable and future proof manner could provide an incredibly useful and large amount
of data. This data, if kept updated, could create endless opportunities for Business Intelligence,
although exactly which ones is beyond the scope of this work. A simple metaphor explains the
potential value of this work: if an oil company were to be told where are all the oil reserves in the
planet, it still should need to invest in machinery, workers and time to successfully exploit them,
but half of the job would have already been done2.
As the reader will see in this work, the way the issue is tackled is by building a somehow complex
architecture that ends in an Artificial Neural Network3. A quick overview of such architecture is
as follows: first find the URLs that lead to the product pages that contain the desired data that
is going to be extracted inside a given site (like URLs that lead to âaction figureâ products inside
the site ebay.com); second, per each URL passed, extract its HTML and make a screenshot of the
page, and store this data in a suitable and scalable fashion; third, label the data that will be fed to
the NN4; fourth, prepare the aforementioned data to be input in an NN; fifth, train the NN; and
sixth, deploy the NN to make [hopefully accurate] predictions
On Web User Tracking: How Third-Party Http Requests Track Users' Browsing Patterns for Personalised Advertising
On today's Web, users trade access to their private data for content and
services. Advertising sustains the business model of many websites and
applications. Efficient and successful advertising relies on predicting users'
actions and tastes to suggest a range of products to buy. It follows that,
while surfing the Web users leave traces regarding their identity in the form
of activity patterns and unstructured data. We analyse how advertising networks
build user footprints and how the suggested advertising reacts to changes in
the user behaviour.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0653
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Group influence on blogs design behaviour
Issues of national culture influence on web design behaviour have been rampant and stimulating on static web pages across the globe. The emergence of a new breed of publication-type web page brought about by the advancement of web technology however, saw a different species of online communication groups. Bloggers as these groups are called; used blogs as their communication and publication tool to distinguish themselves from other websites and online social media users. Since bloggers are groups that are recognised and credited to cultivate their own culture, the idea that national culture has an influence on blogs design behaviour and preferences may have been weakened. Bloggers groups themselves would be the influential factor that determines design preferences of bloggers in a network of blogs. To address the issue, this paper has conducted an assessment on blogs from six countries using content analysis method, national culture traits and SIDE model to ascertain design features characteristics and behaviour. Results from both the global and local blogs in each country showed that blogs design preferences in one country differ between both the global and local bloggers. Furthermore, global bloggers design preferences in countries under observation are found to be similar to one another
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