83,641 research outputs found
Of course we share! Testing Assumptions about Social Tagging Systems
Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior
Challenging Assumptions about IT skills in Higher Education
This paper challenges the idea of 'the digital native' and the subsequent assumption of digital literacy skills amongst higher education students. It offers clear evidence that current student populations come from a wider range of backgrounds than the theory allows for and that the younger student population is also more complex with varying levels of digital literacy experience. It argues that treating students as a homogenous mass is problematic and challenges the idea that generic technology skills are instantly transferable to academic study. The paper concludes with a warning that we are letting down some of our students by the ‘Information Technology (IT) barrier’ within higher education and that we should be focusing on identification of Information Technology (IT) need and IT skills acquisition support rather than assuming it is something students can ‘pick up as they go along’. This will only happen once IT is given the status of a core academic skill along with maths, information literacy and academic communication
Online Model Evaluation in a Large-Scale Computational Advertising Platform
Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing message for a user and to purchase impressions at the right
price, we rely heavily on bid prediction and optimization models. Even though
the bid prediction models are well studied in the literature, the equally
important subject of model evaluation is usually overlooked. Effective and
reliable evaluation of an online bidding model is crucial for making faster
model improvements as well as for utilizing the marketing budgets more
efficiently. In this paper, we present an experimentation framework for bid
prediction models where our focus is on the practical aspects of model
evaluation. Specifically, we outline the unique challenges we encounter in our
platform due to a variety of factors such as heterogeneous goal definitions,
varying budget requirements across different campaigns, high seasonality and
the auction-based environment for inventory purchasing. Then, we introduce
return on investment (ROI) as a unified model performance (i.e., success)
metric and explain its merits over more traditional metrics such as
click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss
commonly used evaluation and metric summarization approaches in detail and
propose a more accurate method for online evaluation of new experimental models
against the baseline. Our meta-analysis-based approach addresses various
shortcomings of other methods and yields statistically robust conclusions that
allow us to conclude experiments more quickly in a reliable manner. We
demonstrate the effectiveness of our evaluation strategy on real campaign data
through some experiments.Comment: Accepted to ICDM201
Information Outlook, September 2004
Volume 8, Issue 9https://scholarworks.sjsu.edu/sla_io_2004/1008/thumbnail.jp
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